Remote Sensing Technology-Based Data Compression Processing Method for Landscape Engineering

Abstract

The present invention relates to the technical field of image data compression, and specifically relates to a method for compressing and processing garden engineering data based on remote sensing technology. This method converts the encoded data corresponding to the gray values in the garden gray-scale image into a one-dimensional data sequence to obtain a garden data sequence; obtains first attention data according to the difference between each gray-scale data and local gray-scale data in the garden data sequence, and screens out second attention data in the garden data sequence from the first attention data according to the similarity between local gray-scale data; obtains the lossy necessity degree of the second attention according to the distribution law of the second attention data in the garden data sequence; and finally optimizes the second attention data according to the lossy necessity degree to obtain an optimal garden data sequence for compression and transmission. The present invention realizes image data compression, improves the compression effect, speeds up the transmission speed, and is conducive to efficient garden surveying.

Description

Technical Field

[0001] The present invention relates to the technical field of image data compression, and particularly to a method for compressing and processing garden engineering data based on remote sensing technology. Background Art

[0002] With the development and innovation of technology, remote sensing technology already has very good object detection accuracy. It realizes remote sensing measurement of the detection object through remote control, including information such as the position, distance, size, and depth of the detection object. After such data information is collected by sensors and undergoes relevant calculations, it is finally displayed through accurate data. Based on the remote sensing method, exploration and data collection of garden engineering designs with certain difficulties can be realized, greatly reducing unnecessary consumption of manpower and financial resources, and improving the accuracy of engineering measurement while increasing the safety of garden engineering surveys.

[0003] Since the volume of garden engineering data obtained by surveying is relatively large, compression processing is often required when storing the current data. Traditional compression methods often use run-length encoding for compression processing. However, run-length encoding compression depends on the data redundancy of the data set. Among the existing methods for optimizing and improving the data redundancy of the data set, the design buildings and the like in garden data are not considered, resulting in image distortion and other situations during optimization compression, and the compression effect is not stable. Furthermore, the transmission process efficiency of remote sensing photography is extremely low, affecting the progress of garden survey projects. Summary of the Invention

[0004] In order to solve the technical problems in the prior art that image distortion and other situations occur during the optimization compression of garden data, the compression effect is not stable, and the transmission process efficiency is extremely low, the purpose of the present invention is to provide a method for compressing and processing garden engineering data based on remote sensing technology. The specific technical solution adopted is as follows:

[0005] The present invention provides a method for compressing and processing garden engineering data based on remote sensing technology. The method includes:

[0006] Obtain a garden grayscale image, and use the encoded data corresponding to all grayscale values in the garden grayscale image as grayscale data; convert all the grayscale data into a one-dimensional data sequence to obtain a garden data sequence;

[0007] Determine first attention data according to the difference degree between each grayscale data and adjacent grayscale data in the garden data sequence; screen out second attention data from the first attention data according to the similarity degree between adjacent grayscale data in the garden data sequence; obtain the lossy necessity degree of the second attention data according to the distribution law characteristics of the second attention data in the garden data sequence;

[0008] Perform a lossy transformation on the second attention data according to the described necessary degree of lossiness to obtain an optimal garden data sequence, and perform compressed transmission on the optimal garden data sequence.

[0009] Furthermore, the method for obtaining the first attention data includes:

[0010] In the garden data sequence, obtain the difference index between grayscale data according to the proximity of the coding data between each grayscale data and its adjacent grayscale data; take the average of all difference indexes corresponding to each grayscale data as the first attention of each grayscale data.

[0011] When the first attention is greater than the preset first attention threshold, use the corresponding grayscale data as the first attention data.

[0012] Furthermore, the method for obtaining the difference index includes:

[0013] Convert two grayscale data into fixed-length binary codes as detection data; assign different position weights to different positions in the binary code of the detection data, perform an exclusive OR operation on the values at the same positions in the binary codes of the two detection data to obtain the exclusive OR value at each position between the binary codes of the two detection data.

[0014] Use the position weight of each position as the weight of the exclusive OR value at each position, calculate the weighted average of the exclusive OR values at each position to obtain the comprehensive exclusive OR value of all positions corresponding to the binary codes between the two detection data, and perform normalization processing on the comprehensive exclusive OR value to obtain the difference index between the two detection data.

[0015] Furthermore, the method for obtaining the second attention data includes:

[0016] When the binary codes corresponding to all positions between two detection data are different, use the corresponding difference index as the maximum difference index.

[0017] Optionally select one first attention data as the reference data. In the garden data sequence, use the grayscale data within the preset distance threshold range of the reference data as the local grayscale data of the reference data; use two local grayscale data with equal distances to the reference data in the garden data sequence as the local data pair corresponding to the reference data; calculate the difference index between the two local grayscale data in each local data pair corresponding to the reference data in the garden data sequence as the difference index of each local data pair; calculate the absolute value of the difference between the maximum difference index corresponding to the reference data and the difference index of each local data pair as the similarity index of each local data pair.

[0018] Calculate the average value of the similarity indexes corresponding to all local gray data of the reference data to obtain the second attention degree of the reference data; when the second attention degree is greater than or equal to the preset second attention threshold, use the corresponding reference data as the second attention degree data.

[0019] Further, the method for obtaining the lossy necessity degree includes:

[0020] In the garden data sequence, count the number of gray data between each second attention degree data and the previous second attention degree data to obtain the previous data amount of each second attention degree data; count the number of gray data between each second attention degree data and the next second attention degree data to obtain the subsequent data amount of each second attention degree data;

[0021] Calculate the absolute value of the difference between the previous data amount and the subsequent data amount of each second attention degree data as the interval amount. When the interval amount is less than or equal to the preset interval threshold, record the lossy necessity degree of the corresponding second attention degree data as the preset first mark value; when the interval amount is greater than the preset interval threshold, record the lossy necessity degree of the corresponding second attention degree data as the preset second mark value.

[0022] Further, the method for obtaining the optimal garden data sequence includes:

[0023] When the lossy necessity degree of the second attention degree data is the preset second mark value, update the data value of the corresponding second attention degree data to the data value of the adjacent gray data, and use the updated garden data sequence of all second attention degree data as the optimal garden data sequence.

[0024] Further, the method for obtaining the garden data sequence includes:

[0025] Convert the gray value of each pixel point in the garden gray image data into binary coded data, and perform raster scanning row by row on all binary coded data in the garden gray image data to construct a one-dimensional data sequence as the garden data sequence.

[0026] The present invention has the following beneficial effects:

[0027] The present invention converts the encoded data corresponding to the gray values in the garden gray-scale image into a one-dimensional data sequence to obtain a garden data sequence for analysis, which facilitates the analysis of the variation characteristics and regularities among garden data. According to the difference between each gray-scale data and its adjacent gray-scale data in the garden data sequence, and the similarity between adjacent gray-scale data, the second attention data in the garden data sequence is screened out. The second attention data is data that can be lossy optimized according to the change of data distribution. Considering the design factors in garden design, the necessary degree of lossiness of the second attention is obtained through the distribution law of the second attention data in the garden data sequence, and the data with garden design information is retained. Finally, the second attention data is optimized according to the necessary degree of lossiness, which improves the data redundancy, obtains the optimal garden data sequence for compression transmission, avoids the problem of image distortion while improving the compression effect, speeds up the transmission speed, and is conducive to efficient garden surveying. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0029] Figure 1 It is a flowchart of a method for compressing and processing garden engineering data based on remote sensing technology provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in combination with the accompanying drawings and preferred embodiments, elaborate in detail on a method for compressing and processing garden engineering data based on remote sensing technology proposed by the present invention, its specific implementation manner, structure, features and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0032] The following will specifically describe the specific solution of a method for compressing and processing garden engineering data based on remote sensing technology provided by the present invention with reference to the accompanying drawings.

[0033] Please refer to Figure 1, which shows a flowchart of a method for compressing and processing garden engineering data based on remote sensing technology provided by an embodiment of the present invention. The method includes the following steps:

[0034] S1: Obtain a garden grayscale image, and use the encoded data corresponding to all grayscale values in the garden grayscale image as grayscale data; convert all grayscale data into a one-dimensional data sequence to obtain a garden data sequence.

[0035] When the present invention uses remote sensing technology to survey garden data, the main focus is on surveying the coverage and distribution degree of various buildings and areas in the garden, that is, mainly calculating the distribution length and area of the regions in the remote sensing image. However, since the survey process uses pre-set drone remote sensing photography, deviation data is very likely to appear in the image, or there are other messy objects in the distribution area. These data have no relation to the measurement accuracy, so a certain amount of lossy compression can be performed to make the key measurement data of the image more accurate and the transmission effect of the compression effect better when performing image compression.

[0036] For image data, the amount of information corresponding to the pixel points in the image is extremely large, which is a huge challenge to the limited storage space and limited transmission speed of the drone remote sensing platform. Since the payload of the drone remote sensing platform is limited, the carried operation processor is generally an embedded controller. The operation and storage capabilities of such controller devices have certain limitations compared with general computers. Therefore, it is more necessary to perform reasonable lossy compression processing on the image in combination with the characteristics of the measurement data required for garden engineering to avoid data expansion and make the efficiency of each survey process higher.

[0037] First, use a drone to remotely sense and photograph the current garden survey image, and perform grayscale processing to obtain a garden grayscale image. To more conveniently analyze the pixel points of the image, the present invention converts all grayscale values in the garden grayscale image into encoded data recognizable by a computer as grayscale data, and calculates the loss degree of some messy data through the data characteristics of the grayscale data. Convert all grayscale data into a one-dimensional data sequence to obtain a garden data sequence, which is convenient for subsequent judgment of data rules. In the embodiment of the present invention, the grayscale value of each pixel point in the garden grayscale image is converted into binary encoded data, which is convenient for computer operation processing. The raster scanning is performed row by row on all the binary encoded data corresponding to the garden grayscale image to construct a one-dimensional data sequence as the garden data sequence. It should be noted that both binary encoding conversion and raster scanning are well-known technical means to those skilled in the art and will not be elaborated here.

[0038] So far, the preliminary preprocessing of the garden grayscale image data is completed, and the garden data sequence can be directly analyzed and processed subsequently.

[0039] S2: Determine the first attention data according to the difference degree between each gray - scale data and its adjacent gray - scale data in the garden data sequence; screen out the second attention data from the first attention data according to the similarity degree between adjacent gray - scale data in the garden data sequence; obtain the lossy necessity degree of the second attention data according to the distribution law characteristics of the second attention data in the garden data sequence.

[0040] Since the main focus of the garden survey in the present invention is to analyze the distribution of various buildings and regional coverage in the garden, the pixel points of the regional distribution in the image are usually the same or similar. Therefore, the original data that can be lost can be analyzed without affecting the overall survey, increasing the redundancy effect of the surveyed area. Since the pixel points in the region have a relatively high similarity, but for other messy pixel points that deviate, there are certain differences between the pixel points and the surrounding pixel points. Therefore, the points with differences are first screened out. In the present invention, the first attention data is determined as the points with differences.

[0041] Determine the first attention data according to the difference degree between each gray - scale data and its adjacent gray - scale data in the garden data sequence. The first attention data is the data that has a certain difference from the surrounding data. Preferably, in the garden data sequence, the calculation method of the difference degree is mainly to perform an exclusive - OR operation on the encoded data. In the embodiment of the present invention, two gray - scale data are converted into fixed - length binary codes. Since the range of gray - scale values is from 0 to 255, the encoded length after conversion to binary code will not exceed 8 bits. Convert the gray - scale data into an 8 - bit binary code for convenient operation. If the number of bits is insufficient, padding is performed before encoding, and the padding data is 2. For example, for the gray - scale data with a binary code of 10110, it is converted into a fixed - length binary code of 22210110. Padding with 2 is to facilitate the accuracy of subsequent exclusive - OR operations. The values therein do not have data meanings, and the size of each gray - scale data has not changed.

[0042] Take the converted encoded data as the detection data, and perform an exclusive - OR operation on the binary codes of the two detection data in terms of position. However, since the detection data represents data with gray - scale value meanings, the difference degrees of the data in different positions of the encoded data are different. For example, for the code 10000000 corresponding to the gray - scale value of 128 and the code 11100000 corresponding to the gray - scale value of 224, if only an exclusive - OR operation is performed, the difference value is small, but the difference in gray - scale values is large. Therefore, different position weights are assigned to different positions in the binary code of the detection data to facilitate the calculation of the difference degree of the binary code to conform to the judgment of the gray - scale difference degree. It should be noted that the exclusive - OR operation and binary - code conversion are both well - known technical means to those skilled in the art and will not be elaborated here.

[0043] Perform an exclusive OR operation on the values at the same positions in the binary encodings corresponding to the two detection data to obtain the exclusive OR value between the binary encodings of the two detection data. The exclusive OR value is a value of 0 or 1. The position weight of each position serves as the weight of the exclusive OR value at each position. Calculate the weighted average of the exclusive OR values at all positions to obtain the comprehensive exclusive OR value corresponding to the two detection data. Normalize the comprehensive exclusive OR value to obtain the difference index between the two detection data. The difference degree between the two detection data is reflected by the difference index. The greater the difference degree, the greater the difference index, and the more different the two detection data are. In the embodiments of the present invention, the specific expression of the difference index is:

[0044]

[0045] In the formula, represents the difference index, represents the fixed length value of the encoding, which is 8 in the embodiments of the present invention; represents the th position weight; represents the data value at the th position in the detection data ; represents the data value at the th position in the detection data ; represents performing an exclusive OR operation on the data value at the th position, that is, the exclusive OR value at the th position; represents the normalization function. It should be noted that normalization is a well-known technical means to those skilled in the art. The selection of the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited herein.

[0046] Among them, represents the comprehensive exclusive OR value corresponding to the detection data and the detection data . In the embodiments of the present invention, the position order of the binary encoding is from left to right as 1 to 8. Therefore, the corresponding position weights are = 0.9, = 0.8, = 0.7, = 0.6, = 0.4, = 0.3, = 0.2, = 0.1. The specific values can be adjusted by the implementer according to the specific implementation situation. For example, for the two data of detection data 10000000 and detection data 22211001, the calculation process of calculating the comprehensive exclusive OR value is (1×0.9 + 1×0.8 + 1×0.7 + 1×0.6 + 1×0.4 + 0×0.3 + 0×0.2 + 1×0.1) = 0.4375. After normalizing the comprehensive exclusive OR value, the difference index between the two detection data can be obtained as 0.875.

[0047] Furthermore, in the garden data sequence, according to the proximity of the encoded data between each grayscale data and its adjacent grayscale data, the difference index between the grayscale data is obtained. Since there are two adjacent grayscale data for each grayscale data, two difference indexes can be calculated for each grayscale data. The average value of all the difference indexes corresponding to each grayscale data is used as the first attention degree of each grayscale data, and the difference degree between each grayscale data and the local grayscale data is reflected through the first attention degree. When the first attention degree is greater than the preset first attention threshold, it indicates that the difference between the corresponding grayscale data and the surrounding grayscale data is relatively large, and the corresponding grayscale data is used as the first attention degree data. In the embodiment of the present invention, the preset first attention threshold is 0.6.

[0048] In other embodiments of the present invention, the equivalence operation can also be used to calculate the proximity of the encoded data between the grayscale data. When the equivalence operation is used for calculation, the obtained equivalence value needs to be finally subjected to a negative correlation mapping to obtain the difference index. The equivalence operation can reflect the similarity degree between the encoded data, and the negative correlation mapping is used to reflect the difference degree between the two encoded data. The equivalence operation is also a well-known technical means for those skilled in the art, and the specific calculation process will not be elaborated here.

[0049] So far, the preliminary screening of the abnormal points is completed. For the points with a large difference from the adjacent grayscale data, at this time, the data situation around each first attention degree data is judged. When the data around the first attention degree data are all relatively similar, it can more explain that the position of the first attention degree is in the garden area and is an offset data that does not affect the measurement. Therefore, according to the similarity degree between the first attention degree data among the adjacent grayscale data in the garden data sequence, the second attention degree data is screened out from the first attention degree data. The second attention degree data is the data that may be the offset data. The specific method is as follows:

[0050] Optionally select a first attention data as the reference data, and analyze the similarity of the first attention data to the adjacent gray data by analyzing the reference data. In the garden data sequence, the gray data within the preset distance threshold range of the reference data is used as the local gray data of the reference data, and two local gray data with equal distances to the reference data are used as the local data pair of the reference data. First, limit the range of the adjacent gray data corresponding to the reference data. In the embodiment of the present invention, the preset distance threshold is 2, that is, the adjacent gray data with a left and right distance of 1 and the adjacent gray data with a left and right distance of 2 to the reference data are both the local gray data of the reference data. At the same time, the adjacent gray data with a left and right distance of 1 can be used as a local data pair, and the adjacent gray data with a left and right distance of 2 can be used as a local data pair. The specific value can be limited by the implementer according to the specific implementation situation and is not limited here.

[0051] Calculate the difference index between the two local gray data in each local data pair corresponding to the reference data in the garden data sequence. Since the distance is less than or equal to the preset distance threshold, that is to say, calculate the difference index between the adjacent gray data with a left and right distance of 1 to the reference data, and the difference index between the adjacent gray data with a left and right distance of 2, which are also the difference indexes obtained by calculating the two local data pairs.

[0052] Since the present invention mainly focuses on the similarity degree between adjacent gray data, but the difference index reflects the difference degree. Therefore, in an embodiment of the present invention, calculate the maximum difference index corresponding to the difference index. The difference between the difference index and the maximum difference index can reflect the similarity degree. The maximum difference index is when all positions of the binary codes corresponding to the two detection data are different, and the corresponding difference index is the maximum difference index, that is, the maximum value of the value range of the difference index, which is 1 in the embodiment of the present invention.

[0053] Calculate the absolute value of the difference between the maximum difference index and the difference index of each local data pair to obtain the similarity index of each local data pair. The similarity index is negatively correlated with the difference index. When the difference index is smaller, it means that the difference degree between the two gray data indexes is smaller, and the similarity degree is larger, so the similarity index is larger. Calculate the average value of the similarity indexes of all local data pairs corresponding to the reference data to obtain the second attention of the reference data. The second attention reflects the similarity degree between the overall adjacent gray data of the reference data. In the embodiment of the present invention, the expression of the second attention is:

[0054]

[0055] In the formula, represents the second attention of the reference data, represents the total number of local data pairs corresponding to the reference data, Denoted as the maximum difference index, Denoted as the difference index of the local data pair corresponding to the nth reference data.

[0056] Among them, Denoted as the similarity index of the local data pair corresponding to the nth reference data. When the difference index is smaller, the similarity index is larger, and the second attention degree corresponding to the reference data is greater, indicating that the reference data is more similar to the adjacent gray-scale data, and the reference data is more likely to be the corresponding deviation data.

[0057] When the second attention degree is greater than or equal to the preset second attention threshold, it indicates that the adjacent gray-scale similarity of the reference data is relatively high, and the reference data may be the deviation data. The corresponding reference data is used as the second attention degree data. In the embodiment of the present invention, the preset second attention threshold is 0.7. In the process of determining the second attention degree data according to the reference data, all the second attention degree data are screened out from the first attention degree data. At this time, the second attention degree data are all those with a large difference between themselves and the surrounding gray-scale data, but a high similarity between the surrounding data. Such data is very likely to be the deviation data that can be transformed.

[0058] When performing conventional run-length compression processing on the image, these deviation data will greatly affect the compression effect. However, the local data of the second attention degree data has strong similarity. The second attention degree data can be converted, and at this time, the compression effect can be improved. However, in the second attention degree data, there may be special building groups in the landscape design, such as special decorative trees in the landscape square. Usually, these special building designs are arranged in a regular relationship. During compression, they cannot be directly ignored, otherwise it will cause a certain degree of distortion and color coverage, affecting the survey results. Therefore, further analyze the distribution of the second attention degree data in the landscape data sequence to obtain the necessary degree of loss for each second attention degree data, and judge the possibility of optimizing the second attention degree data through the necessary degree of loss.

[0059] Preferably, in an embodiment of the present invention, in the garden data sequence, the number of grayscale data between each second attention data and the previous second attention data is counted to obtain the previous data volume of each second attention data, and the number of grayscale data between each second attention data and the next second attention data is counted to obtain the subsequent data volume of each second attention data. The degree of uniform periodic arrangement of each second attention data can be reflected by the similarity between the previous data volume and the subsequent data volume. That is to say, the necessary degree of loss of the second attention data is reflected by the data arrangement rule among three second attention data. It should be noted that since there are no complete front and back second attention data for comprehensive judgment of the first second attention data and the last second attention data, the necessary degree of loss of the first second attention data is the same as that of the next second attention data, and the necessary degree of loss of the last second attention data is the same as that of the previous second attention data.

[0060] The absolute value of the difference between the previous data quantity and the subsequent data volume of each second attention data is calculated as the interval quantity. The interval quantity reflects the regular arrangement of the second attention data. The smaller the interval quantity, the more regular the arrangement. When the interval quantity is less than or equal to the preset interval threshold, it indicates that the arrangement of the second attention data is regular enough, and the corresponding second attention data may be a special garden design with garden-related information. Therefore, the necessary degree of loss of the corresponding second attention data is recorded as the preset first mark value. When the interval quantity is greater than the preset interval threshold, it indicates that the arrangement of the second attention data is not regular enough and there is a certain randomness. At this time, the second attention data can be directly ignored and compressed without affecting the garden survey result, and the necessary degree of loss of the corresponding second attention data is recorded as the preset second mark value. In the embodiment of the present invention, the preset interval threshold is 2, the preset first mark value is 0, and the preset first mark value is 1. The specific values can be adjusted by the implementer and are not limited here.

[0061] After obtaining the necessary degree of loss of the second attention data, the garden data sequence can be optimized according to the final necessary degree of loss.

[0062] S3: Perform lossy transformation on the second attention data according to the necessary degree of loss of the second attention data to obtain the optimal garden data sequence, and compress and transmit the optimal garden data sequence.

[0063] For the data of surveying garden engineering, since the key points of surveying are the calculation of the side lengths and areas of each region of the garden, mainly for the lossless retention of similar pixel points. For the second-concern data, these data points may be caused by pixel value deviation due to remote sensing photography or other reasons, and the analysis of the regular distribution of the second-concern data is added to avoid other objects in the garden design in the second-concern data and prevent affecting the authenticity of the survey results during coverage optimization.

[0064] Perform a lossy transformation on the second-concern data according to the lossy necessity degree of the second-concern data to obtain an optimal garden data sequence. The optimal garden data sequence is the data sequence that can finally be compressed and stored. Preferably, the method for obtaining the optimal garden data sequence is: when the lossy necessity degree of the second-concern data is a preset second marker value, it indicates that the second-concern data is deviated data that can be optimized. Update the data value of the corresponding second-concern data to the data value of the adjacent gray-level data, increase the redundancy of the data, improve the compression efficiency, and use the garden data sequence after updating all the second-concern data as the optimal garden data sequence.

[0065] When compressing the optimal garden data sequence at this time, it can ensure that while increasing the data redundancy, it does not affect the survey results. Perform run-length encoding compression on the optimal garden data sequence, which greatly improves the compression effect, speeds up the platform transmission speed, saves the storage space of the UAV remote sensing system at the same time, and is convenient for the efficient progress of garden survey.

[0066] In summary, the present invention analyzes the garden data sequence by converting the encoded data corresponding to the gray values in the garden gray image into a one-dimensional data sequence, which is convenient for analyzing the change characteristics and regularities among the garden data. According to the difference between each gray-level data and the adjacent gray-level data in the garden data sequence and the similarity between adjacent gray-level data, the second-concern data in the garden data sequence is screened out. The second-concern data is the data that can be lossily optimized according to the change of data distribution. Considering the design factors in garden design, the lossy necessity degree of the second-concern is obtained through the distribution law of the second-concern data in the garden data sequence, and the data with garden design information is retained. Finally, the second-concern data is optimized according to the lossy necessity degree, the redundancy of the data is increased, the optimal garden data sequence is obtained for compressed transmission, the compression effect is improved, the transmission speed is accelerated, which is beneficial to the efficient progress of garden survey.

[0067] It should be noted that: the above-mentioned order of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be beneficial.

[0068] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized respectively.

Claims

1. A garden engineering data compression processing method based on remote sensing technology, characterized in that: The method comprises: Acquire a garden grayscale image, and use the coded data corresponding to all grayscale values ​​in the garden grayscale image as grayscale data; convert all the grayscale data into a one-dimensional data sequence to obtain a garden data sequence; Determine the first attention data according to the difference between each grayscale data and the adjacent grayscale data in the garden data sequence; select the second attention data from the first attention data according to the similarity between the adjacent grayscale data of the first attention data in the garden data sequence; obtain the necessary degree of loss of the second attention data according to the distribution law characteristics of the second attention data in the garden data sequence; Performing lossy transformation on the second attention data according to the necessary lossiness of the second attention data to obtain an optimal garden data sequence, and compressing and transmitting the optimal garden data sequence; The method for acquiring the second attention data includes: When all positions of the binary codes between two detection data are different, the corresponding difference index is used as the maximum difference index; Any first attention data is selected as reference data. In the garden data sequence, the grayscale data of the reference data within a preset distance threshold is used as the local grayscale data of the reference data; two local grayscale data with equal distances to the reference data in the garden data sequence are used as the local data pair corresponding to the reference data; the difference index between the two local grayscale data in each local data pair corresponding to the reference data in the garden data sequence is calculated as the difference index of each local data pair; the absolute value of the difference between the maximum difference index corresponding to the reference data and the difference index between each local data pair is calculated as the similarity index of each local data pair; Calculate the average value of the similarity index of each local data pair corresponding to the reference data to obtain the second attention degree of the reference data; when the second attention degree is greater than or equal to a preset second attention threshold, use the corresponding reference data as the second attention degree data; The second attention degree satisfies the following formula: in, It is expressed as the second attention of the reference data. Represented as the total number of local data pairs corresponding to the reference data, Expressed as a large difference index, Expressed as The difference index of the local data pair corresponding to the reference data.

2. The garden engineering data compression processing method based on remote sensing technology according to claim 1 is characterized in that: The method for acquiring the first attention data includes: In the garden data sequence, the difference index between the grayscale data is obtained according to the proximity of the encoding data between each grayscale data and the adjacent grayscale data; the average value of all the difference indexes corresponding to each grayscale data is taken as the first attention degree of each grayscale data; When the first attention degree is greater than a preset first attention threshold, the corresponding grayscale data is used as the first attention degree data.

3. The garden engineering data compression processing method based on remote sensing technology according to claim 2 is characterized in that: The method for obtaining the difference index includes: Convert the two grayscale data into fixed-length binary codes as detection data; assign different position weights to different positions in the binary codes of the detection data, perform XOR operations on the values ​​at the same position in the binary codes of the two detection data, and obtain the XOR value of each position between the binary codes of the two detection data; The position weight of each position is used as the weight of the XOR value of each position. The XOR value of each position is weighted and averaged to obtain the comprehensive XOR value of all positions corresponding to the binary code between the two detection data. The comprehensive XOR value is normalized to obtain the difference index between the two detection data.

4. The garden engineering data compression processing method based on remote sensing technology according to claim 1 is characterized in that: The acquisition method that is detrimental to the necessary degree includes: In the garden data sequence, the number of grayscale data between each second attention data and the previous second attention data is counted to obtain the amount of previous data of each second attention data; the number of grayscale data between each second attention data and the next second attention data is counted to obtain the amount of subsequent data of each second attention data; The absolute value of the difference between the amount of previous data and the amount of subsequent data of each second attention data is calculated as the number of intervals. When the number of intervals is less than or equal to a preset interval threshold, the necessary degree of loss of the corresponding second attention data is recorded as a preset first marking value; when the number of intervals is greater than the preset interval threshold, the necessary degree of loss of the corresponding second attention data is recorded as a preset second marking value.

5. The garden engineering data compression processing method based on remote sensing technology according to claim 4 is characterized in that: The method for obtaining the optimal garden data sequence includes: When the necessary degree of loss of the second attention data is a preset second mark value, the data value corresponding to the second attention data is updated to the data value of the adjacent grayscale data, and the garden data sequence after all the second attention data are updated is used as the optimal garden data sequence.

6. The garden engineering data compression processing method based on remote sensing technology according to claim 1 is characterized in that: The method for obtaining the garden data sequence comprises: The gray value of each pixel in the garden gray image is converted into binary coded data, all binary coded data corresponding to the garden gray image are raster scanned line by line, and a one-dimensional data sequence is constructed as a garden data sequence.

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