Remote sensing image compression transmission method, system and device under unstable wireless environment

Abstract

The present invention discloses a remote sensing image compression transmission method, system and device in an unstable wireless environment, and relates to the field of data compression technology. First, in a preset monitoring time period, wireless network data and remote sensing image information are monitored and processed in real time, the network environment is dynamically switched according to the network fluctuation value, and the compression rate and sampling rate of the remote sensing image are adjusted, and then the image is compressed, and the transmission data is detected and analyzed during the transmission process. If the quality of the transmitted remote sensing image is low, the receiving end requests the sending end to retransmit the remote sensing image to ensure the data transmission quality, and the image compression rate and sampling rate are intelligently adjusted according to the real-time network status, so as to effectively cope with the instability of the wireless network, ensure that the remote sensing image maintains high quality during the transmission process, and improve the transmission efficiency and reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of data compression, and in particular to a remote sensing image compression transmission method, system and equipment in an unstable wireless environment. Background Art

[0002] In an unstable wireless environment, a variety of technologies are used to compress and transmit high-quality remote sensing images, such as adaptive image compression, error-tolerant transmission protocols, and coding algorithms optimized for wireless network characteristics, to improve the efficiency and quality of image transmission, minimize data loss, reduce transmission redundancy, and ensure the clarity and availability of remote sensing images when the network environment is unstable.

[0003] For example, the invention patent with announcement number CN102523452B announces an image conversion, compression and transmission method, which first converts the original image A into another image B of the same size, which is easier to compress than image A; the sending end performs conventional compression encoding on image B before transmitting it; at the receiving end, it is decompressed and decoded to obtain the restored image B; then image B is mapped using a nonlinear transformation based on a neural network to restore the original image A.

[0004] For example, the invention patent with announcement number CN103797798B announces a captured image compression transmission method and a captured image compression transmission system, which include: a sending side system, which compresses and sends an input image; and a receiving side system, which restores the input image based on the image data received from the sending side system. The captured image compression transmission system is characterized in that the sending side system includes: a sending side storage unit, which stores multiple reference images; a sending side image selection unit, which gradually selects reference images corresponding to the information from the sending side storage unit according to the priority of the information included in the capture information attached to the input image, and specifies a reference image; a differential image compression unit, which obtains differential data between the reference image specified by the sending side image selection unit and the input image, and compresses the differential data; and a transmission unit, which sends the compressed differential data to the receiving side system.

[0005] Based on the above findings, existing technical solutions may suffer from image quality loss, image transmission errors and data loss, and limited transmission efficiency, which may lead to reduced image details and clarity and inaccurate information transmission. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method, system and device for compressing and transmitting remote sensing images in an unstable wireless environment, which solves the problems in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a remote sensing image compression transmission method in an unstable wireless environment, including presetting each monitoring time period, monitoring each wireless network data within each monitoring time period, and collecting information of each remote sensing image.

[0008] The wireless network data of each monitoring time period are processed to obtain the wireless network fluctuation value of each monitoring time period, the wireless network environment is switched according to the wireless network fluctuation value of each monitoring time period, and the compression rate of the remote sensing image is adjusted.

[0009] The information of each remote sensing image is extracted, and the feature conformity value of each remote sensing image in each monitoring time period is obtained by processing. In combination with the network conformity value of each monitoring time period, the adjustment evaluation value of each remote sensing image in each monitoring time period is obtained, and the sampling rate of the remote sensing image is adjusted.

[0010] The compressed remote sensing image is transmitted, the transmission data of the remote sensing image is detected and acquired during the transmission process, and the transmission data of the remote sensing image is analyzed to obtain the transmission quality evaluation value of the remote sensing image, and the transmission data of the remote sensing image is optimized.

[0011] Furthermore, the data of each wireless network in each monitoring time period is processed to obtain the fluctuation value of each wireless network in each monitoring time period. The specific process is: the data of each wireless network is monitored in each monitoring time period to obtain the data of each wireless network in each monitoring time period. The data of each wireless network in each monitoring time period includes: the maximum signal strength, the minimum signal strength, the delay, the average network jitter, the cumulative network disconnection time, and the maximum packet loss rate of each wireless network in each monitoring time period.

[0012] The information of each remote sensing image is collected, and the information of the remote sensing image includes: file size, resolution, average gray value, noise, scale, and pixels of each remote sensing image.

[0013] The maximum and minimum values ​​of the signal strength of each wireless network in each monitoring time period are extracted for difference processing to obtain the signal strength difference of each wireless network in each monitoring time period; the cumulative network disconnection duration of each wireless network in each monitoring time period is extracted and divided by the duration of the monitoring time period to obtain the network disconnection rate of each wireless network in each monitoring time period; the delay, average network jitter and maximum packet loss rate of each wireless network in each monitoring time period are combined to obtain the fluctuation value of each wireless network in each monitoring time period; the fluctuation value of each wireless network in each monitoring time period is used to quantitatively evaluate the stability of each wireless network in each detection time period.

[0014] Furthermore, the wireless network environment is switched according to the wireless network fluctuation values ​​of each monitoring time period, and the compression rate of the remote sensing image is adjusted. The specific process is: extract the wireless network fluctuation values ​​of each monitoring time period and sort them from small to large, select the wireless network corresponding to the wireless network fluctuation value with the first ranking as the wireless network environment for remote sensing image transmission, and match the wireless network fluctuation value with the compression rate adjustment value corresponding to each interval of the wireless network fluctuation value stored in the database to obtain the compression rate adjustment value of the remote sensing image, extract the original compression rate of the remote sensing image and multiply it by the matched compression rate adjustment value of the remote sensing image to obtain the adjusted compression rate of the remote sensing image, and adjust the compression rate of the remote sensing image according to the adjusted compression rate of the remote sensing image.

[0015] Furthermore, information of each remote sensing image is extracted, and processed to obtain a feature conformity value of each remote sensing image in each monitoring time period. The specific process is: extracting the file size, resolution, average gray value, and noise of each remote sensing image, and processing to obtain a feature conformity value of each remote sensing image in each monitoring time period. The feature conformity value of each remote sensing image in each monitoring time period is used to evaluate the quality of each remote sensing image.

[0016] The feature conformity value of each remote sensing image in each monitoring time period is compared with the feature conformity threshold of the remote sensing image stored in the database. If the feature conformity value of a remote sensing image is less than or equal to the feature conformity threshold of the remote sensing image, the remote sensing image corresponding to the feature conformity value of the remote sensing image in the monitoring time period is optimized. If the feature conformity value of a remote sensing image is greater than the feature conformity threshold of the remote sensing image, the remote sensing image corresponding to the feature conformity value of the remote sensing image in the monitoring time period is retained.

[0017] The fluctuation values ​​of each wireless network in each monitoring time period and the characteristic coincidence values ​​of each remote sensing image in each monitoring time period are extracted, and combined with the scale and pixels of each remote sensing image, the adjusted evaluation values ​​of each remote sensing image in each monitoring time period are processed to obtain the adjusted evaluation values ​​of each remote sensing image in each monitoring time period. The adjusted evaluation values ​​of each remote sensing image in each monitoring time period are used to evaluate whether each remote sensing image in each monitoring time period needs to be compressed.

[0018] The adjusted evaluation value of each remote sensing image in each monitoring time period is compared with the adjusted evaluation threshold of the remote sensing image stored in the database. If the adjusted evaluation value of each remote sensing image in a certain monitoring time period is greater than or equal to the adjusted evaluation threshold of the remote sensing image stored in the database, the adjustment compression rate of the remote sensing image is extracted to compress the remote sensing image in this monitoring time period. If the adjusted evaluation value of each remote sensing image in a certain monitoring time period is less than the adjusted evaluation threshold of the remote sensing image stored in the database, there is no need to compress the remote sensing image in this monitoring time period.

[0019] Furthermore, the adjustment evaluation value of each remote sensing image in each monitoring time period is analyzed in the following specific process:

[0020] ;

[0021] In the formula, represents the adjusted evaluation value of the qth remote sensing image in the ith monitoring period, The jth wireless network fluctuation value in the i-th monitoring time period, represents the characteristic compliance value of the qth remote sensing image in the ith monitoring period, represents the scale of the qth remote sensing image in the ith monitoring period, represents the pixel of the qth remote sensing image in the ith monitoring period, Indicates the target scale of the remote sensing image. Represents the target pixel of the remote sensing image. Indicates the weight factor corresponding to the set wireless network fluctuation value. Indicates the weight factor corresponding to the characteristic compliance value of the set remote sensing image, Indicates the weight factor corresponding to the scale of the set remote sensing image, It represents the weight factor corresponding to the pixel of the set remote sensing image, i represents the number of each monitoring time period, , m represents the total number of monitoring time periods, j represents the number of each wireless network, , n represents the total number of wireless networks, q represents the number of each remote sensing image, , r represents the total number of remote sensing images.

[0022] Furthermore, an adjusted evaluation value of each remote sensing image in each monitoring time period is obtained, and the sampling rate of the remote sensing image is adjusted. The specific process is: extract the adjusted evaluation value of each remote sensing image in each monitoring time period, match it with the sampling rate corresponding to each interval of the adjusted evaluation value of the remote sensing image stored in the database, obtain the sampling rate of each remote sensing image in each monitoring time period, and sample the remote sensing image according to the sampling rate of each remote sensing image in each monitoring time period.

[0023] Furthermore, the transmission data of the remote sensing image during the transmission process is detected and analyzed. Specifically, the remote sensing image after compression sampling is transmitted, and the receiving end monitors the transmission data of the remote sensing image after receiving the remote sensing image. The transmission data of the remote sensing image includes: the variance and average gradient of the remote sensing image.

[0024] The packet loss rate of each remote sensing image transmission process is extracted, and the variance and average gradient of the remote sensing image are extracted, and the transmission quality assessment values ​​of each remote sensing image are obtained by processing. The transmission quality assessment values ​​of each remote sensing image are used to evaluate the quality of the remote sensing image transmission process.

[0025] Furthermore, the transmission quality assessment value of the remote sensing image is obtained, and the transmission data of the remote sensing image is optimized. The specific analysis is as follows: the transmission quality assessment value of each remote sensing image is extracted and compared with the transmission quality assessment threshold of the remote sensing image in the database. If the transmission quality assessment value of a remote sensing image is lower than the transmission quality assessment threshold of the remote sensing image in the database, the receiving end sends a signal to the sending end to request retransmission of the remote sensing image. If the transmission quality assessment value of a remote sensing image is higher than or equal to the transmission quality assessment threshold of the remote sensing image in the database, there is no need to request retransmission of the remote sensing image.

[0026] Furthermore, the remote sensing image compression transmission system in an unstable wireless environment includes: a network monitoring module for presetting each monitoring time period, monitoring each wireless network data within each monitoring time period, and collecting information of each remote sensing image.

[0027] The network fluctuation processing module is used to process the wireless network data of each monitoring time period, obtain the wireless network fluctuation value of each monitoring time period, switch the wireless network environment according to the wireless network fluctuation value of each monitoring time period, and adjust the compression rate of the remote sensing image.

[0028] The image compression module is used to extract the information of each remote sensing image, process it to obtain the feature compliance value of each remote sensing image in each monitoring time period, and combine it with the network compliance value of each monitoring time period to obtain the adjustment evaluation value of each remote sensing image in each monitoring time period, and adjust the sampling rate of the remote sensing image.

[0029] The retransmission request module is used to transmit the compressed remote sensing image, detect and obtain the transmission data of the remote sensing image during the transmission process, analyze the transmission data of the remote sensing image, obtain the transmission quality evaluation value of the remote sensing image, and optimize the transmission of the transmission data of the remote sensing image.

[0030] Furthermore, a remote sensing image compression and transmission device in an unstable wireless environment: the device includes a processor, a memory, and a network interface connected to the processor and the memory, the memory is used to store data and programs; the network interface is connected to the processor and the memory, is responsible for data communication with the wireless network, and can be connected to a remote server; the processor is used to perform computing tasks of image compression, transmission and network environment adjustment, and is responsible for processing and calculating image data.

[0031] The present invention has the following beneficial effects:

[0032] (1) The present invention monitors and processes wireless network data and remote sensing image information in real time within a preset monitoring time period, dynamically switches the network environment according to the network fluctuation value and adjusts the compression rate and sampling rate of the remote sensing image, then compresses the image, and detects and analyzes the transmission data during the transmission process. If the quality of the transmitted remote sensing image is low, the receiving end requests the sending end to retransmit the remote sensing image to ensure the data transmission quality. The image compression rate and sampling rate are intelligently adjusted according to the real-time network status, effectively coping with the instability of the wireless network, ensuring that the remote sensing image maintains high quality during the transmission process, while improving the transmission efficiency and reliability;

[0033] (2) The present invention monitors the wireless network data in each monitoring time period and collects remote sensing image information to obtain the fluctuation value of each wireless network in each monitoring time period for quantitative evaluation of the stability of each wireless network in each detection time period, and selects the wireless network environment for remote sensing image transmission, which helps to improve the reliability and stability of remote sensing image transmission and provides strong support for the real-time transmission and efficient use of remote sensing images;

[0034] (3) The present invention obtains the characteristic conformity value of each remote sensing image in each monitoring time period, evaluates the quality of each remote sensing image, optimizes images with poor quality, avoids the transmission of unnecessary low-quality image data, extracts the fluctuation value of each wireless network in each monitoring time period and the characteristic conformity value of each remote sensing image in each monitoring time period, and combines the scale and pixel of each remote sensing image to obtain the adjusted evaluation value of each remote sensing image in each monitoring time period, obtains the sampling rate of the remote sensing image, reduces the amount of data to be transmitted, and helps to improve the efficiency of image transmission;

[0035] (4) The receiving end of the present invention monitors the transmission data, processes and obtains the transmission quality evaluation value of each remote sensing image, and evaluates the quality of the transmitted remote sensing image. When the transmission quality does not meet the standard, the receiving end can automatically request retransmission, which helps to improve the success rate of image transmission and provides reliable data for subsequent applications of remote sensing images.

[0036] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the process of the present invention;

[0038] Figure 2 is a remote sensing image variance-remote sensing image transmission quality assessment value curve of the present invention;

[0039] Figure 3 It is a schematic diagram of the system flow of the present invention;

[0040] Figure 4It is the overall network structure diagram of the remote sensing image compression and transmission model used in the present invention;

[0041] Figure 5 It is a detailed network structure diagram of the semantic decoder in the remote sensing image compression and transmission model adapted in the present invention;

[0042] Figure 6 It is a detailed network structure diagram of the semantic decoder in the remote sensing image compression and transmission model adapted in the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inside", "all around" and the like indicating orientation or positional relationship are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0045] See also Figure 1 The embodiment of the present invention provides a technical solution: a remote sensing image compression transmission method in an unstable wireless environment, including presetting each monitoring time period, monitoring each wireless network data in each monitoring time period, and collecting information of each remote sensing image.

[0046] The wireless network data of each monitoring time period are processed to obtain the wireless network fluctuation value of each monitoring time period, the wireless network environment is switched according to the wireless network fluctuation value of each monitoring time period, and the compression rate of the remote sensing image is adjusted.

[0047] The information of each remote sensing image is extracted, and the feature conformity value of each remote sensing image in each monitoring time period is obtained by processing. In combination with the network conformity value of each monitoring time period, the adjustment evaluation value of each remote sensing image in each monitoring time period is obtained, and the sampling rate of the remote sensing image is adjusted.

[0048] The compressed remote sensing image is transmitted, the transmission data of the remote sensing image is detected and acquired during the transmission process, and the transmission data of the remote sensing image is analyzed to obtain the transmission quality evaluation value of the remote sensing image, and the transmission data of the remote sensing image is optimized.

[0049] It should be noted that within the preset monitoring time period, the wireless network data and remote sensing image information are monitored and processed in real time. The network environment is dynamically switched according to the network fluctuation value and the compression rate and sampling rate of the remote sensing image are adjusted. The image is then compressed and the transmission data is detected and analyzed during the transmission process. If the quality of the transmitted remote sensing image is low, the receiving end requests the sending end to retransmit the remote sensing image to ensure the quality of data transmission. The image compression rate and sampling rate are intelligently adjusted according to the real-time network conditions to effectively cope with the instability of the wireless network, ensure that the remote sensing image maintains high quality during the transmission process, and improve the transmission efficiency and reliability. By extracting important semantic features of the image, it emphasizes the transmission of meaningful information, thereby giving priority to preserving the expected semantic content of the source.

[0050] It should be noted that the remote sensing image compression and transmission method, system and device in an unstable wireless environment also include collecting historical remote sensing image data, constructing a data set, and dividing the data set into a training set, a verification set and a test set; using the training set to train a high-quality remote sensing image compression and transmission model; wherein the remote sensing image model includes a semantic encoder, a position mask module, a residual calculation module, an analog channel, a semantic decoder and a residual enhancement model connected in sequence; the semantic encoder is used to extract the semantic features of the remote sensing image; the position mask module is used to compress the semantic information according to the current channel bandwidth ratio; the residual calculation module is used to obtain the residual information between the original remote sensing image data and the rough reconstructed image data; the analog channel is used to simulate noise interference on the output of the position mask module and the residual information, and transmit the interfered encoding results to the semantic decoder and the residual enhancement model respectively; during the training process, the parameters of the analog channel remain unchanged; the semantic decoder uses the received encoding results to reconstruct the rough original remote sensing image; the residual enhancement model is used to restore the residual information; and finally the rough original remote sensing image and the residual information are combined to obtain the reconstructed original remote sensing image.

[0051] It should be noted that if Figure 4 As shown, Figure 4 The present invention provides an overall network structure diagram of the remote sensing image compression and transmission model used in the present invention, including a remote sensing image compression and transmission model, wherein the remote sensing image compression and transmission model comprises six parts, namely, a trainable semantic encoder, a non-trainable position mask compression module, a non-trainable residual calculation module, a non-trainable simulation channel, a trainable semantic decoder and a trainable residual enhancement model connected in sequence.

[0052] It should be noted that if Figure 6 As shown, Figure 6 is a detailed network structure diagram of the semantic decoder in the remote sensing image compression and transmission model adapted in the present invention, including the implemented residual enhancement model, wherein Figure 6The detailed network structure diagram of the semantic decoder in the remote sensing image compression and transmission model adapted in the present invention includes three residual convolution modules, each of which is followed by a feature attention module; the last feature attention module is followed by a first 2D convolution layer and a second 2D convolution layer, the first 2D convolution layer is connected to a ReLU activation function, and the second 2D convolution layer is connected to a Sigmoid activation function, which normalizes the output to a [0, 1] range that conforms to the standard image intensity value.

[0053] It should be noted that the ReLU activation function is a linear rectification function, which is a commonly used activation function in artificial neural networks. It usually refers to nonlinear functions represented by ramp functions and their variants. The Sigmoid activation function is a commonly used activation function in neural networks. Artificial neural networks are a type of mathematical model that simulates the structure and function of the human brain's nervous system.

[0054] It should be noted that the semantic encoder includes multiple residual convolution modules connected in sequence, and two adjacent residual convolution modules are connected through an attention feature module; its specific structure is as follows: the residual convolution module includes a first 2D convolution layer and a second 2D convolution layer, the first 2D convolution layer is connected to a first generalized normalization layer, the second 2D convolution layer is connected to a second generalized normalization layer, and the first generalized normalization layer is connected to a PeReLU activation layer.

[0055] It should be noted that the semantic decoder also includes a reshaping module, which is connected to multiple transposed residual convolution modules, and two adjacent transposed residual convolution modules are connected through an attention feature module; the reshaping module is used to restore the original semantic space dimension from the compressed vector form.

[0056] It should be noted that if Figure 5 As shown, Figure 5 It is a detailed network structure diagram of the semantic decoder in the remote sensing image compression and transmission model adapted in the present invention, including a semantic decoder module, which includes a reshaping module connected in sequence and a pair of five groups of attention feature modules and transposed convolution modules. The last transposed convolution module has a slight difference in activation function compared with the previous transposed convolution module.

[0057] It should be noted that The transposed residual convolution module consists of two cascaded 2D transposed convolution layers, each of which is connected to a normalization layer, and the output of each normalization layer is used as the input of the PeReLU activation function; , is the number of transposed convolution modules. The last transposed convolution module includes two cascaded 2D transposed convolution layers. Each 2D transposed convolution layer is connected to a normalization layer. The output of the first normalization layer is used as the input of the PeReLU activation function, and the output of the second normalization layer is used as the input of the Sigmoid activation function.

[0058] It should be noted that the residual enhancement model includes three residual convolution modules, each of which is followed by a feature attention module; the last feature attention module is followed by a first 2D convolution layer and a second 2D convolution layer, the first 2D convolution layer is connected to a ReLU activation layer, and the second 2D convolution layer is connected to a Sigmoid activation layer.

[0059] Specifically, each wireless network data is monitored during each monitoring time period to obtain each wireless network data during each monitoring time period, and the each wireless network data during each monitoring time period includes: the maximum signal strength, minimum signal strength, delay, average network jitter, cumulative network disconnection duration, and maximum packet loss rate of each wireless network during each monitoring time period.

[0060] The information of each remote sensing image is collected, and the information of the remote sensing image includes: file size, resolution, average gray value, noise, scale, and pixels of each remote sensing image.

[0061] The maximum and minimum values ​​of the signal strength of each wireless network in each monitoring time period are extracted for difference processing to obtain the signal strength difference of each wireless network in each monitoring time period; the cumulative network disconnection duration of each wireless network in each monitoring time period is extracted and divided by the duration of the monitoring time period to obtain the network disconnection rate of each wireless network in each monitoring time period; the delay, average network jitter and maximum packet loss rate of each wireless network in each monitoring time period are combined to obtain the fluctuation value of each wireless network in each monitoring time period; the fluctuation value of each wireless network in each monitoring time period is used to quantitatively evaluate the stability of each wireless network in each detection time period.

[0062] It should be noted that historical remote sensing image data is collected, a data set is constructed, and the remote sensing image information in the data set is extracted.

[0063] It should be noted that the signal monitoring software (such as NetSpot, inSSIDer) is used to continuously monitor the signal strength of each wireless network and generate a report to obtain the maximum and minimum values ​​of the signal strength of each wireless network within a period; professional network monitoring software (such as Wireshark, Network Monitor) is used to obtain network delay; network jitter and packet loss rate are monitored and reported in real time through network monitoring platforms (such as SolarWinds, PRTG, Nagios), the network jitter of each wireless network in each monitoring period is averaged to obtain the average network jitter of each wireless network in each monitoring time period, and the maximum packet loss rate of each wireless network in each monitoring period is taken to obtain the maximum packet loss rate of each wireless network in each monitoring time period; network monitoring software is used to monitor the network connection status in real time, and the network disconnection duration in a time period is recorded and accumulated to obtain the cumulative network disconnection duration of each wireless network in each monitoring time period, and the cumulative network disconnection duration of each wireless network in each monitoring time period is divided by the duration of the monitoring time period to obtain the network disconnection rate of each wireless network in each monitoring time period. The network disconnection rate represents the proportion of network connection interruption in a period of time.

[0064] It should be noted that directly reading the information of remote sensing images can reduce the file size of remote sensing images; through professional remote sensing image processing software (such as ENVI The Environment for Visualizing Images as a remote sensing image processing platform, ERDAS Imagine as a remote sensing image processing system), the resolution, grayscale value of each pixel, noise, scale, and pixel of the remote sensing image can be obtained; the grayscale value of each pixel in the remote sensing image is averaged to obtain the average grayscale value. The scale represents the proportional relationship between the represented object size and the actual ground size.

[0065] It should be noted that the specific analysis process of the fluctuation value of each wireless network in each monitoring time period is as follows:

[0066] ;

[0067] represents the jth wireless network fluctuation value in the i-th monitoring time period, represents the signal strength difference of the jth wireless network in the i-th monitoring time period, represents the disconnection rate of the jth wireless network in the i-th monitoring time period, represents the delay of the jth wireless network in the i-th monitoring time period, represents the average network jitter of the jth wireless network in the ith monitoring period, represents the maximum packet loss rate of the jth wireless network in the i-th monitoring time period, Indicates the maximum permissible difference in signal strength of the wireless network. Indicates the maximum allowed value of the wireless network disconnection rate. Indicates the reference value corresponding to the delay of the set wireless network. Indicates the reference value corresponding to the average network jitter of the set wireless network. It represents the reference value corresponding to the maximum packet loss rate of the wireless network. e is a natural constant. Indicates the correction factor corresponding to the signal strength difference of the set wireless network. Indicates the correction factor corresponding to the disconnection rate of the set wireless network. Indicates the correction factor corresponding to the delay of the set wireless network. Indicates the correction factor corresponding to the average network jitter of the set wireless network. It indicates the correction factor corresponding to the maximum packet loss rate of the wireless network. i indicates the number of each monitoring time period. , m represents the total number of monitoring time periods, j represents the number of each wireless network, , n represents the total number of wireless networks.

[0068] It should be noted that there is a correlation between the signal strength difference, disconnection rate, delay, average network jitter and maximum packet loss rate of the wireless network. When the signal strength difference is larger, the stability of the wireless network is reduced, which leads to an increase in the disconnection rate; when the average network jitter is large, the data packet arrival time is relatively long and the delay will also increase; when the maximum packet loss rate is high, it means that a large number of data packets are lost during the data transmission process of the wireless network, which may cause the network connection to be unstable or even interrupted, thereby increasing the disconnection rate. The wireless network fluctuation value is determined by the signal strength difference, disconnection rate, delay, average network jitter and maximum packet loss rate of the wireless network;

[0069] The influence factor corresponding to the signal strength difference of the wireless network set in the database represents the numerical value of the influence degree of the signal strength difference of the wireless network on the fluctuation value of the wireless network. When used, the influence factor corresponding to the signal strength difference of the wireless network can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the signal strength difference of the wireless network and the influence factor corresponding to the signal strength difference of the wireless network preset in the database form a mapping set. The real-time signal strength difference of the wireless network is input into the mapping set to obtain the influence factor corresponding to the signal strength difference of the wireless network. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1];

[0070] The influence factor corresponding to the wireless network disconnection rate set in the database represents the numerical value of the influence degree of the wireless network disconnection rate on the wireless network fluctuation value. When used, the influence factor corresponding to the wireless network disconnection rate can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the wireless network disconnection rate and the influence factor corresponding to the wireless network disconnection rate preset in the database form a mapping set, and the real-time wireless network disconnection rate is input into the mapping set to obtain the influence factor corresponding to the wireless network disconnection rate. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1];

[0071] The influence factor corresponding to the delay of the wireless network set in the database represents the numerical value of the influence degree of the delay of the wireless network on the fluctuation value of the wireless network. When used, the influence factor corresponding to the delay of the wireless network can be directly obtained from the database, and its corresponding relationship can be a pre-set mapping relationship. For example, the delay of the wireless network and the influence factor corresponding to the delay of the wireless network preset in the database form a mapping set, and the real-time delay of the wireless network is input into the mapping set to obtain the influence factor corresponding to the delay of the wireless network. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1];

[0072] The impact factor corresponding to the average network jitter of the wireless network set in the database represents the numerical value of the impact degree of the average network jitter of the wireless network on the fluctuation value of the wireless network. When used, the impact factor corresponding to the average network jitter of the wireless network can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the average network jitter of the wireless network and the impact factor corresponding to the average network jitter of the wireless network preset in the database form a mapping set. The real-time average network jitter of the wireless network is input into the mapping set to obtain the impact factor corresponding to the average network jitter of the wireless network. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1];

[0073] The influence factor corresponding to the maximum packet loss rate of the wireless network set in the database represents the numerical value of the influence degree of the maximum packet loss rate of the wireless network on the fluctuation value of the wireless network. When used, the influence factor corresponding to the maximum packet loss rate of the wireless network can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the maximum packet loss rate of the wireless network and the influence factor corresponding to the maximum packet loss rate of the wireless network preset in the database form a mapping set. The real-time maximum packet loss rate of the wireless network is input into the mapping set to obtain the influence factor corresponding to the maximum packet loss rate of the wireless network. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0074] It should be noted that quantitatively evaluating the stability of each wireless network in each monitoring time period can provide specific and objective data support, making the evaluation result more reliable.

[0075] Specifically, the wireless network fluctuation values ​​of each monitoring time period are extracted and sorted from small to large, the wireless network corresponding to the wireless network fluctuation value ranked first is selected as the wireless network environment for remote sensing image transmission, and the wireless network fluctuation value is matched with the compression rate adjustment value corresponding to each interval of the wireless network fluctuation value stored in the database to obtain the compression rate adjustment value of the remote sensing image, the original compression rate of the remote sensing image is extracted and multiplied by the matched compression rate adjustment value of the remote sensing image to obtain the adjusted compression rate of the remote sensing image, and the compression rate of the remote sensing image is adjusted according to the adjusted compression rate of the remote sensing image.

[0076] It should be noted that by extracting the wireless network fluctuation values ​​of each monitoring time period and sorting them from small to large, determining the wireless network with the smallest fluctuation as the preferred network environment for remote sensing image transmission, matching the wireless network fluctuation value with the compression rate corresponding to each interval of the wireless network fluctuation value stored in the database includes: the compression rate corresponding to each interval of the wireless network fluctuation value is preset in the database, and the compression rate corresponding to each interval of the wireless network fluctuation value can be directly obtained from the database when used, and the corresponding relationship can be a preset mapping relationship, for example, each wireless network fluctuation value of each monitoring time period and the compression rate corresponding to each interval of the wireless network fluctuation value in the database form a mapping set, and the real-time wireless network fluctuation values ​​of each monitoring time period are input into the mapping set to obtain the compression rate corresponding to each interval of the wireless network fluctuation value, wherein the mapping relationship can be a one-to-one corresponding relationship, and the compression rate of the remote sensing image is obtained. Through professional remote sensing software (such as ENVI The Environment for Visualizing Images as a remote sensing image processing platform and ERDAS Imagine as a remote sensing image processing system), the original compression rate of the remote sensing image can be directly obtained, and the original compression rate of the remote sensing image is multiplied by the matching compression rate to calculate the final adjusted compression rate, thereby optimizing the image compression effect.

[0077] Specifically, the file size, resolution, average gray value, and noise of each remote sensing image are extracted, and the feature conformity values ​​of each remote sensing image in each monitoring time period are obtained by processing. The feature conformity values ​​of each remote sensing image in each monitoring time period are used to evaluate the quality of each remote sensing image.

[0078] The feature conformity value of each remote sensing image in each monitoring time period is compared with the feature conformity threshold of the remote sensing image stored in the database. If the feature conformity value of a remote sensing image is less than or equal to the feature conformity threshold of the remote sensing image, the remote sensing image corresponding to the feature conformity value of the remote sensing image in the monitoring time period is optimized. If the feature conformity value of a remote sensing image is greater than the feature conformity threshold of the remote sensing image, the remote sensing image corresponding to the feature conformity value of the remote sensing image in the monitoring time period is retained.

[0079] The fluctuation values ​​of each wireless network in each monitoring time period and the characteristic coincidence values ​​of each remote sensing image in each monitoring time period are extracted, and combined with the scale and pixels of each remote sensing image, the adjusted evaluation values ​​of each remote sensing image in each monitoring time period are processed to obtain the adjusted evaluation values ​​of each remote sensing image in each monitoring time period. The adjusted evaluation values ​​of each remote sensing image in each monitoring time period are used to evaluate whether each remote sensing image in each monitoring time period needs to be compressed.

[0080] The adjusted evaluation value of each remote sensing image in each monitoring time period is compared with the adjusted evaluation threshold of the remote sensing image stored in the database. If the adjusted evaluation value of each remote sensing image in a certain monitoring time period is greater than or equal to the adjusted evaluation threshold of the remote sensing image stored in the database, the adjustment compression rate of the remote sensing image is extracted to compress the remote sensing image in this monitoring time period. If the adjusted evaluation value of each remote sensing image in a certain monitoring time period is less than the adjusted evaluation threshold of the remote sensing image stored in the database, there is no need to compress the remote sensing image in this monitoring time period.

[0081] It should be noted that the characteristic conformity values ​​of each remote sensing image in each monitoring period are analyzed in the following specific process:

[0082] ;

[0083] represents the characteristic compliance value of the qth remote sensing image in the ith monitoring period, represents the file size of the qth remote sensing image in the ith monitoring period, represents the resolution of the qth remote sensing image in the ith monitoring period, represents the average gray value of the qth remote sensing image in the ith monitoring period, represents the noise of the qth remote sensing image in the ith monitoring period, Indicates the target file size of the remote sensing image. Indicates the target resolution value of the remote sensing image. Indicates the reference value corresponding to the average gray value of the set remote sensing image. Indicates the reference value corresponding to the noise of the set remote sensing image. Indicates the correction factor corresponding to the file size of the set remote sensing image. Indicates the correction factor corresponding to the resolution of the set remote sensing image. Indicates the correction factor corresponding to the average gray value of the set remote sensing image. It represents the correction factor corresponding to the noise of the set remote sensing image, i represents the number of each monitoring time period, , m represents the total number of monitoring time periods, q represents the number of each remote sensing image, , r represents the total number of remote sensing images.

[0084] It should be noted that the file size, resolution, average grayscale value, and noise of each remote sensing image are correlated. Since high-resolution images contain more pixels, they are more likely to capture tiny noises. Therefore, the higher the resolution, the larger the image noise will be. The higher the image resolution, the larger the file size will be. High-resolution images may bring more obvious noise, which may increase the file size. Noise may cause the grayscale value of the image to deviate from the original value and increase the average grayscale of the image. The feature conformity value of the remote sensing image is jointly determined by the file size, resolution, average grayscale value, and noise of the remote sensing image.

[0085] The impact factor corresponding to the file size of the remote sensing image set in the database represents the numerical value of the impact of the file size of the remote sensing image on the feature compliance value of the remote sensing image. When used, the impact factor corresponding to the file size of the remote sensing image can be directly obtained from the database. The corresponding relationship can be a preset mapping relationship. For example, the file size of the remote sensing image and the impact factor corresponding to the file size of the remote sensing image preset in the database form a mapping set. The file size of the real-time remote sensing image is input into the mapping set to obtain the impact factor corresponding to the file size of the remote sensing image. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1];

[0086] The impact factor corresponding to the resolution of the remote sensing image set in the database represents the numerical value of the degree of influence of the resolution of the remote sensing image on the feature conformity value of the remote sensing image. When used, the impact factor corresponding to the resolution of the remote sensing image can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the resolution of the remote sensing image and the impact factor corresponding to the resolution of the remote sensing image preset in the database form a mapping set. The resolution of the real-time remote sensing image is input into the mapping set to obtain the impact factor corresponding to the resolution of the remote sensing image. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1];

[0087] The influence factor corresponding to the average grayscale value of the remote sensing image set in the database represents the numerical value of the influence of the average grayscale value of the remote sensing image on the feature conformity value of the remote sensing image. When used, the influence factor corresponding to the average grayscale value of the remote sensing image can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the average grayscale value of the remote sensing image and the influence factor corresponding to the average grayscale value of the remote sensing image preset in the database form a mapping set. The average grayscale value of the real-time remote sensing image is input into the mapping set to obtain the influence factor corresponding to the average grayscale value of the remote sensing image. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1];

[0088] The influence factor corresponding to the noise of the remote sensing image set in the database represents the numerical value of the influence degree of the noise of the remote sensing image on the characteristic compliance value of the remote sensing image. When used, the influence factor corresponding to the noise of the remote sensing image can be directly obtained from the database. The corresponding relationship can be a preset mapping relationship. For example, the noise of the remote sensing image and the influence factor corresponding to the noise of the remote sensing image preset in the database form a mapping set, and the noise of the real-time remote sensing image is input into the mapping set to obtain the influence factor corresponding to the noise of the remote sensing image. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0089] It should be noted that optimizing the remote sensing image corresponding to the feature conformity value of the remote sensing image in the monitoring time period includes: extracting the feature conformity value of each remote sensing image in each monitoring time period and comparing it with the feature conformity threshold of the remote sensing image stored in the database; if the feature conformity value of a remote sensing image is less than or equal to the feature conformity threshold of the remote sensing image, processing the image through the Sobel operator to enhance the sharpening effect of the image. The Sobel operator is an important operator in the field of image processing. The remote sensing image is optimized. If the feature conformity value of a remote sensing image is greater than the feature conformity threshold of the remote sensing image, no processing is required. By obtaining the feature conformity value of each remote sensing image in each monitoring time period, the feature conformity values ​​of the remote sensing images are compared and optimized. The system can effectively identify and retain high-quality remote sensing images, thereby avoiding the transmission of unnecessary low-quality image data, which helps to improve the reliability of remote sensing image transmission.

[0090] It should be noted that, in combination with the wireless network fluctuation value, the remote sensing image feature compliance value, the scale and the pixel, the adjusted evaluation value of each remote sensing image in each monitoring time period is processed and compared with the adjusted evaluation threshold of the remote sensing image in the database. If the adjusted evaluation value of each remote sensing image in a monitoring time period is greater than or equal to the adjusted evaluation threshold of the remote sensing image stored in the database, the adjusted compression rate is extracted, and the remote sensing image of the monitoring time period is compressed. If the adjusted evaluation value of each remote sensing image in a monitoring time period is less than the adjusted evaluation threshold of the remote sensing image stored in the database, there is no need to compress the remote sensing image of the monitoring time period. By judging whether each remote sensing image in each monitoring time period needs to be compressed, it helps to avoid the loss of details in the compression process of low-quality remote sensing images and maintain the high definition of the image.

[0091] Specifically, the adjusted evaluation value of each remote sensing image in each monitoring time period, the specific analysis process is as follows:

[0092] ;

[0093] In the formula, represents the adjusted evaluation value of the qth remote sensing image in the ith monitoring period, The jth wireless network fluctuation value in the i-th monitoring time period, represents the characteristic compliance value of the qth remote sensing image in the ith monitoring period, represents the scale of the qth remote sensing image in the ith monitoring period, represents the pixel of the qth remote sensing image in the ith monitoring period, Indicates the target scale of the remote sensing image. Represents the target pixel of the remote sensing image. Indicates the weight factor corresponding to the set wireless network fluctuation value. Indicates the weight factor corresponding to the characteristic compliance value of the set remote sensing image, Indicates the weight factor corresponding to the scale of the set remote sensing image, It represents the weight factor corresponding to the pixel of the set remote sensing image, i represents the number of each monitoring time period, , m represents the total number of monitoring time periods, j represents the number of each wireless network, , n represents the total number of wireless networks, q represents the number of each remote sensing image, , r represents the total number of remote sensing images.

[0094] It should be noted that The weight factor corresponding to the wireless network fluctuation value set in the database represents the numerical value of the influence degree of the wireless network fluctuation value on the adjustment evaluation value of the remote sensing image. When used, the influence factor corresponding to the wireless network fluctuation value can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the wireless network fluctuation value and the weight factor corresponding to the wireless network fluctuation value preset in the database form a mapping set. The real-time wireless network fluctuation value is input into the mapping set to obtain the weight factor corresponding to the wireless network fluctuation value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1];

[0095] The weight factor corresponding to the feature conformity value of the remote sensing image set in the database represents the numerical value of the influence of the feature conformity value of the remote sensing image on the adjustment evaluation value of the remote sensing image. When used, the influence factor corresponding to the feature conformity value of the remote sensing image can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the feature conformity value of the remote sensing image and the weight factor corresponding to the feature conformity value of the remote sensing image preset in the database form a mapping set. The feature conformity value of the real-time remote sensing image is input into the mapping set to obtain the weight factor corresponding to the feature conformity value of the remote sensing image. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1];

[0096] The weight factor corresponding to the scale of the remote sensing image set in the database represents the numerical value of the influence of the scale of the remote sensing image on the adjustment evaluation value of the remote sensing image. When used, the influence factor corresponding to the scale of the remote sensing image can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the scale of the remote sensing image and the weight factor corresponding to the scale of the remote sensing image preset in the database form a mapping set. The scale of the real-time remote sensing image is input into the mapping set to obtain the weight factor corresponding to the scale of the remote sensing image. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1];

[0097] The weight factor corresponding to the pixel of the remote sensing image set in the database represents the numerical value of the influence degree of the pixel of the remote sensing image on the adjustment evaluation value of the remote sensing image. When used, the influence factor corresponding to the pixel of the remote sensing image can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the pixels of the remote sensing image and the weight factors corresponding to the pixels of the remote sensing image preset in the database form a mapping set, and the pixels of the real-time remote sensing image are input into the mapping set to obtain the weight factors corresponding to the pixels of the remote sensing image. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0098] It should be noted that the adjustment evaluation value of the remote sensing image is jointly determined by the wireless network fluctuation value, the feature conformity value of the remote sensing image, the scale of the remote sensing image, and the pixels of the remote sensing image.

[0099] Specifically, the adjusted evaluation value of each remote sensing image in each monitoring time period is extracted, matched with the sampling rate corresponding to each interval of the adjusted evaluation value of the remote sensing image stored in the database, to obtain the sampling rate of each remote sensing image in each monitoring time period, and the remote sensing images are sampled according to the sampling rate of each remote sensing image in each monitoring time period.

[0100] It should be noted that the adjusted evaluation value of each remote sensing image in each monitoring time period is extracted and matched with the sampling rate corresponding to each interval of the adjusted evaluation value of the remote sensing image in the database. The sampling rate of each remote sensing image is preset in the database. When used, the sampling rate of each remote sensing image can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the adjusted evaluation value of each remote sensing image in each monitoring time period and the sampling rate of the remote sensing image in the database form a mapping set, and the real-time adjusted evaluation value of each remote sensing image in each monitoring time period is input into the mapping set to obtain the sampling rate of each remote sensing image. The mapping relationship can be a one-to-one relationship. By dynamically adjusting the adjusted evaluation value of the remote sensing image and the corresponding sampling rate, it is possible to better adapt to the instability of the wireless network, reduce the amount of data that needs to be transmitted, and help improve the efficiency of image transmission.

[0101] Specifically, the remote sensing image after compression sampling is transmitted, and the receiving end monitors the transmission data of the remote sensing image after receiving the remote sensing image. The transmission data of the remote sensing image includes: the variance and average gradient of the remote sensing image.

[0102] The packet loss rate of each remote sensing image transmission process is extracted, and the variance and average gradient of the remote sensing image are extracted, and the transmission quality assessment values ​​of each remote sensing image are obtained by processing. The transmission quality assessment values ​​of each remote sensing image are used to evaluate the quality of the remote sensing image transmission process.

[0103] It should be noted that the variance and gradient of remote sensing images can be directly obtained through geographic information system software (such as Quantum GIS), and the gradient of the remote sensing image can be averaged to obtain the average gradient of the remote sensing image. The average gradient refers to the rate of change of the grayscale value in the image. The variance of the remote sensing image is the degree to which the overall grayscale value of the image deviates from its mean. The average gradient of the remote sensing image usually represents the average value of the gradient size of all pixels in the image. The gradient of the remote sensing image refers to the rate of change of the grayscale value of the image. The receiving end refers to the equipment or system responsible for receiving and processing the remote sensing image data transmitted from the sending end.

[0104] It should be noted that the specific analysis process of each remote sensing image transmission quality assessment value is as follows:

[0105] ;

[0106] In the formula, represents the transmission quality assessment value of the qth remote sensing image, represents the variance of the qth remote sensing image, represents the average gradient of the qth remote sensing image, represents the packet loss rate of the qth remote sensing image transmission process, Indicates the variance reference value of the set remote sensing image, Indicates the average gradient reference value of the set remote sensing image. Indicates the allowed packet loss rate during the remote sensing image transmission process. Indicates the correction factor corresponding to the variance of the set remote sensing image, Indicates the correction factor corresponding to the average gradient of the set remote sensing image, It represents the correction factor corresponding to the packet loss rate of the remote sensing image transmission process, q represents the number of each remote sensing image, , r represents the total number of remote sensing images.

[0107] It should be noted that there is a correlation between the variance, average gradient, noise, and packet loss rate of the remote sensing image during transmission. Increased noise usually leads to increased variance of the image, thereby affecting the average gradient of the remote sensing image. Excessive packet loss rate during transmission will lead to increased noise in the image, thereby affecting the image quality. The remote sensing image transmission quality assessment value is jointly determined by the variance, average gradient, noise, and packet loss rate of the remote sensing image during transmission.

[0108] The influence factor corresponding to the variance of the remote sensing image preset in the database represents the numerical value of the influence degree of the variance of the remote sensing image on the remote sensing image transmission quality assessment value. When used, the influence factor corresponding to the variance of the remote sensing image can be directly obtained from the database. The corresponding relationship can be a preset mapping relationship. For example, the variance of the remote sensing image and the influence factor corresponding to the variance of the remote sensing image preset in the database form a mapping set. The variance of the real-time remote sensing image is input into the mapping set to obtain the influence factor corresponding to the variance of the remote sensing image. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1];

[0109] The influence factor corresponding to the average gradient of the remote sensing image preset in the database represents the numerical value of the influence degree of the average gradient of the remote sensing image on the remote sensing image transmission quality assessment value. When used, the influence factor corresponding to the average gradient of the remote sensing image can be directly obtained from the database. The corresponding relationship can be a preset mapping relationship. For example, the average gradient of the remote sensing image and the influence factor corresponding to the average gradient of the remote sensing image preset in the database form a mapping set. The average gradient of the real-time remote sensing image is input into the mapping set to obtain the influence factor corresponding to the average gradient of the remote sensing image. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1];

[0110] The impact factor corresponding to the packet loss rate of the remote sensing image transmission process preset in the database represents the numerical value of the impact degree of the packet loss rate of the remote sensing image transmission process on the remote sensing image transmission quality assessment value. When used, the impact factor corresponding to the packet loss rate of the remote sensing image transmission process can be directly obtained from the database. The corresponding relationship can be a preset mapping relationship. For example, the packet loss rate of the remote sensing image transmission process and the impact factor corresponding to the packet loss rate of the remote sensing image transmission process preset in the database form a mapping set. The packet loss rate of the real-time remote sensing image transmission process is input into the mapping set to obtain the impact factor corresponding to the packet loss rate of the remote sensing image transmission process. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0,1].

[0111] like Figure 2 As shown, Figure 2 The variance of the remote sensing image-the transmission quality assessment value curve of the remote sensing image is shown in Figure 1, where the x-axis represents the variance of the qth remote sensing image and the y-axis represents the transmission quality assessment value of the qth remote sensing image. Three different sets of example parameters are defined in the figure, corresponding to different situations of the three curves, represented by solid lines, dashed lines and dotted lines, respectively. The corresponding curve labels are a, b, and c, respectively. Assuming that the packet loss rate of the qth remote sensing image transmission process is =1, set the variance reference value of the remote sensing image =1000, the average gradient reference value of the remote sensing image is set =20, set the allowable value of packet loss rate in remote sensing image transmission process =0.5, the correction factor corresponding to the variance of the remote sensing image is set =0.44, the correction factor corresponding to the average gradient of the remote sensing image is set =0.62, the correction factor corresponding to the packet loss rate of the remote sensing image transmission process =0.71, when the average gradient of the qth remote sensing image is 12, the schematic diagram of the transmission quality evaluation value of the qth remote sensing image is shown in curve a, when the distance of the average gradient of the qth remote sensing image is 18, the schematic diagram of the transmission quality evaluation value of the qth remote sensing image is shown in curve b, and when the average gradient of the qth remote sensing image is 25, the schematic diagram of the transmission quality evaluation value of the qth remote sensing image is shown in curve c.

[0112] As shown in Table 1, Table 1 is sample data of remote sensing image transmission quality assessment values, which lists the remote sensing image transmission quality assessment values ​​and the variance of the remote sensing image.

[0113] Table 1 Example data of remote sensing image transmission quality evaluation value:

[0114] ;

[0115] As shown in Table 1, in a specific embodiment, assuming that the packet loss rate of the qth remote sensing image transmission process is =1, set the variance reference value of the remote sensing image =1000, the average gradient reference value of the remote sensing image is set =20, set the allowable value of packet loss rate in remote sensing image transmission process =0.5, the correction factor corresponding to the variance of the remote sensing image is set =0.44, the correction factor corresponding to the average gradient of the remote sensing image is set =0.62, the correction factor corresponding to the packet loss rate of the remote sensing image transmission process =0.71, the obstacle hazard assessment value on the UAV flight path is determined by the height of the obstacle on the UAV flight path, the distance between the UAV and the obstacle, and the flight speed of the UAV.

[0116] Specifically, the transmission quality assessment value of each remote sensing image is extracted and compared with the transmission quality assessment threshold of the remote sensing image in the database. If the transmission quality assessment value of a remote sensing image is lower than the transmission quality assessment threshold of the remote sensing image in the database, the receiving end sends a signal to the sending end requesting retransmission of the remote sensing image. If the transmission quality assessment value of a remote sensing image is higher than or equal to the transmission quality assessment threshold of the remote sensing image in the database, there is no need to request retransmission of the remote sensing image.

[0117] It should be noted that the transmission quality assessment value of each remote sensing image is compared with the transmission quality assessment threshold of the remote sensing image in the database. If the transmission quality assessment value of a remote sensing image is lower than the transmission quality assessment threshold of the remote sensing image in the database, the receiving end sends a retransmission request. The sending end will retransmit the remote sensing image after receiving the request. After receiving the retransmitted remote sensing image, the receiving end will perform quality assessment again. If the requirements are met, a confirmation signal will be sent to the sending end, indicating that the remote sensing image has been successfully received. If the transmission quality assessment value of a remote sensing image is higher than or equal to the transmission quality assessment threshold of the remote sensing image in the database, the receiving end will directly send a confirmation signal to the sending end to confirm that the remote sensing image has been successfully received. Through accurate transmission quality assessment and retransmission mechanism, the integrity and accuracy of remote sensing images in transmission are ensured, which helps to improve transmission efficiency and reduce transmission costs.

[0118] Specifically, in this embodiment, the present invention provides a remote sensing image compression transmission system in an unstable wireless environment, including: a network monitoring module for presetting each monitoring time period, monitoring each wireless network data within each monitoring time period, and collecting information of each remote sensing image.

[0119] The network fluctuation processing module is used to process the wireless network data of each monitoring time period, obtain the wireless network fluctuation value of each monitoring time period, switch the wireless network environment according to the wireless network fluctuation value of each monitoring time period, and adjust the compression rate of the remote sensing image.

[0120] The image compression module is used to extract the information of each remote sensing image, process it to obtain the feature compliance value of each remote sensing image in each monitoring time period, and combine it with the network compliance value of each monitoring time period to obtain the adjustment evaluation value of each remote sensing image in each monitoring time period, and adjust the sampling rate of the remote sensing image.

[0121] The retransmission request module is used to transmit the compressed remote sensing image, detect and obtain the transmission data of the remote sensing image during the transmission process, analyze the transmission data of the remote sensing image, obtain the transmission quality evaluation value of the remote sensing image, and optimize the transmission of the transmission data of the remote sensing image.

[0122] It should be noted that the remote sensing image compression transmission system in an unstable wireless environment includes modules such as network monitoring, fluctuation processing, image compression, and retransmission request. It monitors the wireless network status through a preset monitoring time period and adjusts the compression and transmission strategy of the remote sensing image according to network fluctuations, thereby optimizing the transmission quality of remote sensing images in an unstable wireless environment, effectively responding to network fluctuations, ensuring the efficient transmission of high-quality remote sensing images, and improving the image quality and transmission stability during the transmission process. It can be applied to image transmission tasks in various situations.

[0123] Specifically, in this embodiment, the present invention provides a remote sensing image compression and transmission device in an unstable wireless environment, wherein the device includes a processor, a memory, and a network interface connected to the processor and the memory, wherein the memory is used to store data and programs; the network interface is connected to the processor and the memory, and is responsible for data communication with the wireless network, and can be connected to a remote server; the processor is used to perform computing tasks of image compression, transmission, and network environment adjustment, and is responsible for processing and calculating image data.

[0124] It should be noted that the present invention provides a remote sensing image compression and transmission device in an unstable wireless environment, including a processor, a memory and a network interface connected thereto. The memory is used to store data and programs, the network interface is responsible for data communication with the wireless network and connecting to a remote server, and the processor performs computing tasks such as image compression, transmission and network environment adjustment, processes image data and performs related calculations. In an unstable wireless environment, through an efficient image compression algorithm and a dynamic network environment adjustment mechanism, the packet loss rate and delay in the data transmission process can be reduced, thereby ensuring the high quality and stability of image transmission.

[0125] It should be noted that the remote sensing image compression transmission method in an unstable wireless environment provided by the present invention also includes a database for storing and managing wireless network data and remote sensing image information. In this embodiment, the database is used to store a set maximum allowable value of the signal strength difference of the wireless network, a set maximum allowable value of the network disconnection rate of the wireless network, a reference value corresponding to the set delay of the wireless network, a reference value corresponding to the set average network jitter of the wireless network, a reference value corresponding to the set maximum packet loss rate of the wireless network, a correction factor corresponding to the signal strength difference of the wireless network, a correction factor corresponding to the set network disconnection rate of the wireless network, a correction factor corresponding to the set delay of the wireless network, a correction factor corresponding to the set average network jitter of the wireless network, a correction factor corresponding to the set maximum packet loss rate of the wireless network, a feature compliance threshold of the remote sensing image, a set file size target value of the remote sensing image, a set resolution target value of the remote sensing image, and a set average grayscale value of the remote sensing image. corresponding reference value, reference value corresponding to the set noise of the remote sensing image, correction factor corresponding to the set file size of the remote sensing image, correction factor corresponding to the set resolution of the remote sensing image, correction factor corresponding to the set average gray value of the remote sensing image, correction factor corresponding to the set noise of the remote sensing image, adjustment evaluation threshold of the remote sensing image, target scale of the remote sensing image, target pixel of the remote sensing image, weight factor corresponding to the set wireless network fluctuation value, weight factor corresponding to the set feature compliance value of the remote sensing image, weight factor corresponding to the set scale of the remote sensing image, weight factor corresponding to the set pixel of the remote sensing image, variance reference value of the remote sensing image, average gradient reference value of the remote sensing image, allowable value of packet loss rate in the transmission process of the remote sensing image, correction factor corresponding to the variance of the remote sensing image, correction factor corresponding to the average gradient of the remote sensing image, correction factor corresponding to the packet loss rate in the transmission process of the remote sensing image.

[0126] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0127] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A remote sensing image compression transmission method in an unstable wireless environment, characterized in that: include: Preset each monitoring time period, monitor each wireless network data within each monitoring time period, and collect information of each remote sensing image; Processing the wireless network data of each monitoring time period to obtain the wireless network fluctuation value of each monitoring time period, switching the wireless network environment according to the wireless network fluctuation value of each monitoring time period, and adjusting the compression rate of the remote sensing image; Extract the information of each remote sensing image, process it to obtain the feature compliance value of each remote sensing image in each monitoring time period, and combine it with the network compliance value of each monitoring time period to obtain the adjustment evaluation value of each remote sensing image in each monitoring time period, and adjust the sampling rate of the remote sensing image; The compressed remote sensing image is transmitted, the transmission data of the remote sensing image is detected and acquired during the transmission process, and the transmission data of the remote sensing image is analyzed to obtain the transmission quality evaluation value of the remote sensing image, and the transmission data of the remote sensing image is optimized.

2. According to claim 1, the remote sensing image compression transmission method in an unstable wireless environment is characterized by: The wireless network data of each monitoring time period is processed to obtain the fluctuation value of each wireless network in each monitoring time period. The specific process is as follows: The wireless network data is monitored in each monitoring time period to obtain the wireless network data in each monitoring time period, wherein the wireless network data in each monitoring time period includes: the maximum signal strength, the minimum signal strength, the delay, the average network jitter, the cumulative network disconnection time, and the maximum packet loss rate of each wireless network in each monitoring time period; Collecting information of each remote sensing image, wherein the information of the remote sensing image includes: file size, resolution, average gray value, noise, scale, and pixels of each remote sensing image; The maximum and minimum values ​​of the signal strength of each wireless network in each monitoring time period are extracted for difference processing to obtain the signal strength difference of each wireless network in each monitoring time period; the cumulative network disconnection duration of each wireless network in each monitoring time period is extracted and divided by the duration of the monitoring time period to obtain the network disconnection rate of each wireless network in each monitoring time period; the delay, average network jitter and maximum packet loss rate of each wireless network in each monitoring time period are combined to obtain the fluctuation value of each wireless network in each monitoring time period; the fluctuation value of each wireless network in each monitoring time period is used to quantitatively evaluate the stability of each wireless network in each detection time period.

3. According to claim 1, the remote sensing image compression transmission method in an unstable wireless environment is characterized by: The wireless network environment is switched according to the fluctuation values ​​of each wireless network in each monitoring time period, and the compression rate of the remote sensing image is adjusted. The specific process is as follows: Extract the wireless network fluctuation values ​​of each monitoring time period and sort them from small to large, select the wireless network corresponding to the wireless network fluctuation value with the first ranking as the wireless network environment for remote sensing image transmission, match the wireless network fluctuation value with the compression rate adjustment value corresponding to each interval of the wireless network fluctuation value stored in the database, obtain the compression rate adjustment value of the remote sensing image, extract the original compression rate of the remote sensing image and multiply it by the matched compression rate adjustment value of the remote sensing image to obtain the adjusted compression rate of the remote sensing image, and adjust the compression rate of the remote sensing image according to the adjusted compression rate of the remote sensing image.

4. According to claim 1, the remote sensing image compression transmission method in an unstable wireless environment is characterized by: The information of each remote sensing image is extracted and processed to obtain the characteristic compliance value of each remote sensing image in each monitoring time period. The specific process is as follows: Extract the file size, resolution, average gray value, and noise of each remote sensing image, and process them to obtain the characteristic conformity value of each remote sensing image in each monitoring time period, wherein the characteristic conformity value of each remote sensing image in each monitoring time period is used to evaluate the quality of each remote sensing image; The feature conformity value of each remote sensing image in each monitoring time period is compared with the feature conformity threshold of the remote sensing image stored in the database. If the feature conformity value of a remote sensing image is less than or equal to the feature conformity threshold of the remote sensing image, the remote sensing image corresponding to the feature conformity value of the remote sensing image in the monitoring time period is optimized. If the feature conformity value of a remote sensing image is greater than the feature conformity threshold of the remote sensing image, the remote sensing image corresponding to the feature conformity value of the remote sensing image in the monitoring time period is retained. Extracting the fluctuation value of each wireless network in each monitoring time period and the characteristic coincidence value of each remote sensing image in each monitoring time period, and combining the scale and pixel of each remote sensing image to obtain the adjustment evaluation value of each remote sensing image in each monitoring time period, wherein the adjustment evaluation value of each remote sensing image in each monitoring time period is used to evaluate whether each remote sensing image in each monitoring time period needs to be compressed; The adjusted evaluation value of each remote sensing image in each monitoring time period is compared with the adjusted evaluation threshold of the remote sensing image stored in the database. If the adjusted evaluation value of each remote sensing image in a certain monitoring time period is greater than or equal to the adjusted evaluation threshold of the remote sensing image stored in the database, the adjustment compression rate of the remote sensing image is extracted to compress the remote sensing image in this monitoring time period. If the adjusted evaluation value of each remote sensing image in a certain monitoring time period is less than the adjusted evaluation threshold of the remote sensing image stored in the database, there is no need to compress the remote sensing image in this monitoring time period.

5. According to claim 4, the remote sensing image compression transmission method in an unstable wireless environment is characterized in that: The specific analysis process of the adjusted evaluation value of each remote sensing image in each monitoring time period is as follows: ； In the formula, represents the adjusted evaluation value of the qth remote sensing image in the ith monitoring period, The jth wireless network fluctuation value in the i-th monitoring time period, represents the characteristic compliance value of the qth remote sensing image in the ith monitoring period, represents the scale of the qth remote sensing image in the ith monitoring period, represents the pixel of the qth remote sensing image in the ith monitoring period, Indicates the target scale of the remote sensing image. Represents the target pixel of the remote sensing image. Indicates the weight factor corresponding to the set wireless network fluctuation value. Indicates the weight factor corresponding to the characteristic compliance value of the set remote sensing image, Indicates the weight factor corresponding to the scale of the set remote sensing image, It represents the weight factor corresponding to the pixel of the set remote sensing image, i represents the number of each monitoring time period, , m represents the total number of monitoring time periods, j represents the number of each wireless network, , n represents the total number of wireless networks, q represents the number of each remote sensing image, , r represents the total number of remote sensing images.

6. According to claim 3, the remote sensing image compression transmission method in an unstable wireless environment is characterized by: The adjustment evaluation value of each remote sensing image in each monitoring time period is obtained, and the sampling rate of the remote sensing image is adjusted. The specific process is as follows: The adjusted evaluation value of each remote sensing image in each monitoring time period is extracted, and matched with the sampling rate corresponding to each interval of the adjusted evaluation value of the remote sensing image stored in the database to obtain the sampling rate of each remote sensing image in each monitoring time period, and the remote sensing images are sampled according to the sampling rate of each remote sensing image in each monitoring time period.

7. According to claim 1, the remote sensing image compression transmission method in an unstable wireless environment is characterized by: The detection acquires the transmission data of the remote sensing image during the transmission process, and analyzes the transmission data of the remote sensing image. The specific analysis is as follows: The remote sensing image after compression sampling is transmitted, and the receiving end monitors the transmission data of the remote sensing image after receiving the remote sensing image, and the transmission data of the remote sensing image includes: the variance and average gradient of the remote sensing image; The packet loss rate of each remote sensing image transmission process is extracted, and the variance and average gradient of the remote sensing image are extracted, and the transmission quality assessment values ​​of each remote sensing image are obtained by processing. The transmission quality assessment values ​​of each remote sensing image are used to evaluate the quality of the remote sensing image transmission process.

8. According to claim 1, the remote sensing image compression transmission method in an unstable wireless environment is characterized by: The transmission quality evaluation value of the remote sensing image is obtained, and the transmission data of the remote sensing image is optimized. The specific analysis is as follows: The transmission quality assessment value of each remote sensing image is extracted and compared with the transmission quality assessment threshold of the remote sensing image in the database. If the transmission quality assessment value of a remote sensing image is lower than the transmission quality assessment threshold of the remote sensing image in the database, the receiving end sends a signal to the sending end to request retransmission of the remote sensing image. If the transmission quality assessment value of a remote sensing image is higher than or equal to the transmission quality assessment threshold of the remote sensing image in the database, there is no need to request retransmission of the remote sensing image.

9. A remote sensing image compression transmission system in an unstable wireless environment, characterized in that: include: The network monitoring module is used to preset each monitoring time period, monitor each wireless network data within each monitoring time period, and collect information of each remote sensing image; The network fluctuation processing module is used to process the wireless network data of each monitoring time period, obtain the wireless network fluctuation value of each monitoring time period, switch the wireless network environment according to the wireless network fluctuation value of each monitoring time period, and adjust the compression rate of the remote sensing image; The image compression module is used to extract the information of each remote sensing image, process it to obtain the feature compliance value of each remote sensing image in each monitoring time period, and combine it with the network compliance value of each monitoring time period to obtain the adjustment evaluation value of each remote sensing image in each monitoring time period, and adjust the sampling rate of the remote sensing image; The retransmission request module is used to transmit the compressed remote sensing image, detect and obtain the transmission data of the remote sensing image during the transmission process, analyze the transmission data of the remote sensing image, obtain the transmission quality evaluation value of the remote sensing image, and optimize the transmission of the transmission data of the remote sensing image.

10. A device using the remote sensing image compression transmission method in an unstable wireless environment as claimed in any one of claims 1 to 8, characterized in that: The device includes a processor, a memory, and a network interface connected to the processor and the memory, wherein the memory is used to store data and programs; the network interface is connected to the processor and the memory, responsible for data communication with the wireless network, and can be connected to a remote server; the processor is used to perform computing tasks such as image compression, transmission, and network environment adjustment, and is responsible for processing and calculating image data.

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