Image content analysis processing system using positioning big data
By downloading positioning data from remote sensing satellites and combining it with content sharpening, filtering equipment, and radial basis function neural network models, the system automatically selects the optimal enhancement algorithm to process remote sensing images. This solves the problem of matching the optimal algorithm to remote sensing images from different times, and achieves intelligent image processing and improved image quality.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-03
- Publication Date
- 2026-04-14
AI Technical Summary
The optimal enhancement algorithm needs to be matched with remote sensing images of the same scenic area obtained at different times, which is difficult to achieve with existing technology.
Positioning data is downloaded from remote sensing satellites via a signal transmission interface. Images are processed using content sharpening equipment, guided filtering equipment, and combined filtering equipment. In conjunction with network learning equipment and radial basis function neural network models, the contrast of different enhancement algorithms is compared, and the optimal enhancement algorithm is selected for processing.
It enables the automatic selection of the best enhancement algorithm based on different remote sensing image content, thereby improving the intelligence and effectiveness of image processing.
Smart Images
Figure HDA0004646644750000011 
Figure HDA0004646644750000012 
Figure HDA0004646644750000021
Abstract
Description
Technical Field
[0001] This invention relates to the field of image processing, and more particularly to an image content parsing and processing system that utilizes location big data. Background Technology
[0002] Remote sensing satellites can cover the entire Earth or any designated area within a specified time. When operating in geostationary orbit, they can continuously remotely sense a specific region on the Earth's surface. All remote sensing satellites require ground stations. Satellite data obtained from remote sensing platforms can monitor agriculture, forestry, oceanography, land resources, environmental protection, meteorology, and other conditions. Remote sensing satellites are mainly classified into three types: meteorological satellites, land satellites, and ocean satellites.
[0003] In practical applications, the remote sensing images of scenic spots obtained at different times have different content, and therefore require different enhancement algorithms to obtain the best image quality. How to match the optimal enhancement algorithm for different time-sharing remote sensing images of scenic spots is one of the technical challenges that needs to be solved. Summary of the Invention
[0004] To address the aforementioned problems, this invention provides an image content parsing and processing system utilizing location-based big data, the system comprising:
[0005] A signal transmission interface is used to download remote sensing images corresponding to the location data of the scenic area coverage from remote sensing satellites based on the location data of the scenic area coverage, and output them as scenic area coverage images. The location data of the scenic area coverage includes the location information of each scenic spot in the scenic area.
[0006] A content sharpening device, connected to the signal transmission interface, is used to perform spatial differential sharpening processing on the received scenic area overlay image to obtain and output the corresponding content sharpened image.
[0007] A guided filtering device, connected to the content sharpening device, is used to perform guided filtering processing on the received content sharpening image to obtain and output a corresponding guided filtering image;
[0008] A combined filtering device, connected to the guided filtering device, is used to perform combined filtering processing on the received guided filtering image to obtain and output the corresponding combined filtering image;
[0009] A data judgment device, connected to the combined filtering device, includes a network learning device, a content analysis device, and an effect prediction device. The effect prediction device is connected to both the network learning device and the content analysis device. The data judgment device is used to acquire each background pixel of the combined filtered image, determine the coordinate value and brightness value of each background pixel, analyze the signal-to-noise ratio and the number of noise types in the combined filtered image, input the signal-to-noise ratio and the number of noise types in the combined filtered image, the binary algorithm code value corresponding to the enhancement algorithm, the coordinate value and brightness value of each background pixel in the combined filtered image into a radial basis function neural network model, and execute the radial basis function neural network model to obtain the contrast of the image after enhancement operation obtained by the combined filtered image using the set enhancement algorithm. The radial basis function neural network model is a radial basis function neural network that has undergone multiple training actions, and the number of training actions is proportional to the number of noise types.
[0010] A dynamic processing device, connected to the data judgment device, is used to compare the contrast ratios corresponding to various enhancement algorithms, and select the enhancement algorithm corresponding to the smallest contrast ratio as the preferred enhancement algorithm to perform enhancement processing on the combined filtered image to obtain the final enhanced image.
[0011] Among them, various enhancement algorithms include histogram equalization enhancement algorithm, logarithmic image enhancement algorithm, exponential image enhancement algorithm, and high contrast preservation enhancement algorithm.
[0012] The image content analysis and processing system utilizing big data positioning of this invention is intelligent and easy to operate. Because it can compare the contrast ratios of various enhancement algorithms after passing through a radial basis function neural network model, it selects the enhancement algorithm with the lowest contrast ratio as the preferred enhancement algorithm to perform enhancement processing on the combined filtered image, thereby obtaining the optimal content enhancement mode suitable for the specific content of different images. Attached Figure Description
[0013] The embodiments of the present invention will now be described with reference to the accompanying drawings.
[0014] Figure 1 This is a block diagram illustrating the structure of an image content parsing and processing system utilizing location big data according to Embodiment 1 of the present invention.
[0015] Figure 2 This is a block diagram illustrating the structure of an image content parsing and processing system utilizing location big data according to Embodiment 2 of the present invention.
[0016] Figure 3This is a block diagram illustrating the structure of an image content parsing and processing system utilizing location big data according to Embodiment 3 of the present invention. Detailed Implementation
[0017] The embodiments of the image content parsing and processing system utilizing location big data of the present invention will now be described in detail with reference to the accompanying drawings.
[0018] Example 1
[0019] Figure 1 The above is a structural block diagram of an image content parsing and processing system utilizing location big data according to Embodiment 1 of the present invention. The system includes:
[0020] A signal transmission interface is used to download remote sensing images corresponding to the location data of the scenic area coverage from remote sensing satellites based on the location data of the scenic area coverage, and output them as scenic area coverage images. The location data of the scenic area coverage includes the location information of each scenic spot in the scenic area.
[0021] For example, a SOC chip can be used to implement the signal transmission interface, which is used to download remote sensing images corresponding to the location data of the scenic area coverage from remote sensing satellites based on the location data of the scenic area coverage, and output them as scenic area coverage images. The location data of the scenic area coverage includes the location information of each scenic spot corresponding to the scenic area.
[0022] A content sharpening device, connected to the signal transmission interface, is used to perform spatial differential sharpening processing on the received scenic area overlay image to obtain and output the corresponding content sharpened image.
[0023] A guided filtering device, connected to the content sharpening device, is used to perform guided filtering processing on the received content sharpening image to obtain and output a corresponding guided filtering image;
[0024] A combined filtering device, connected to the guided filtering device, is used to perform combined filtering processing on the received guided filtering image to obtain and output the corresponding combined filtering image;
[0025] A data judgment device, connected to the combined filtering device, includes a network learning device, a content analysis device, and an effect prediction device. The effect prediction device is connected to both the network learning device and the content analysis device. The data judgment device is used to acquire each background pixel of the combined filtered image, determine the coordinate value and brightness value of each background pixel, analyze the signal-to-noise ratio and the number of noise types in the combined filtered image, input the signal-to-noise ratio and the number of noise types in the combined filtered image, the binary algorithm code value corresponding to the enhancement algorithm, the coordinate value and brightness value of each background pixel in the combined filtered image into a radial basis function neural network model, and execute the radial basis function neural network model to obtain the contrast of the image after enhancement operation obtained by the combined filtered image using the set enhancement algorithm. The radial basis function neural network model is a radial basis function neural network that has undergone multiple training actions, and the number of training actions is proportional to the number of noise types.
[0026] A dynamic processing device, connected to the data judgment device, is used to compare the contrast ratios corresponding to various enhancement algorithms, and select the enhancement algorithm corresponding to the smallest contrast ratio as the preferred enhancement algorithm to perform enhancement processing on the combined filtered image to obtain the final enhanced image.
[0027] Among them, various enhancement algorithms include histogram equalization enhancement algorithm, logarithmic image enhancement algorithm, exponential image enhancement algorithm and high contrast preservation enhancement algorithm;
[0028] The process involves inputting the signal-to-noise ratio and the number of noise types in the combined filtered image, the binary algorithm code value corresponding to the enhancement algorithm, the coordinate values and brightness values of each background pixel in the combined filtered image, into a radial basis function neural network (RBN) model and executing the RBN model to obtain the contrast of the image after enhancement operation by the RBN model, which is obtained by enhancing the combined filtered image using the set enhancement algorithm. The RBN model is a RBN that has undergone multiple training operations, and the number of training operations is proportional to the number of noise types. The process includes: using a simulation mode to complete the modeling process of the RBN model.
[0029] Example 2
[0030] Figure 2 This is a block diagram illustrating the structure of an image content parsing and processing system utilizing location big data according to Embodiment 2 of the present invention.
[0031] Compared to Figure 1 , Figure 2 The image content parsing and processing system that utilizes location big data may also include:
[0032] A height sensor is connected to the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, respectively, and is used to measure the current real-time height values of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, respectively.
[0033] The height sensor is connected to the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device, respectively, and is used to measure the current real-time height values of each of the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device. The height sensor includes multiple height measurement units, each connected to the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device, to measure the current real-time height values of each of the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device.
[0034] The height sensor device includes multiple height measurement units, which are respectively connected to the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device to separately measure the current real-time height values of each of the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device. The multiple height measurement units are multiple height sensors, which are respectively connected to the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device to separately measure the current real-time height values of each of the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device.
[0035] The plurality of height measurement units are plurality of height sensors, which are respectively connected to the content sharpening device, the guided filtering device, the combined filtering device and the data judgment device to complete the separate measurement of the current real-time height values of the content sharpening device, the guided filtering device, the combined filtering device and the data judgment device, including: the plurality of height sensors have the same structure;
[0036] The plurality of height measurement units are plurality of height sensors, which are respectively connected to the content sharpening device, the guided filtering device, the combined filtering device and the data judgment device to complete the separate measurement of the current real-time height values of the content sharpening device, the guided filtering device, the combined filtering device and the data judgment device. The plurality of height sensors have the same upper limit value and lower limit value for height measurement.
[0037] Example 3
[0038] Figure 3 This is a block diagram illustrating the structure of an image content parsing and processing system utilizing location big data according to Embodiment 3 of the present invention.
[0039] Compared to Figure 1 , Figure 3 The image content parsing and processing system that utilizes location big data may also include:
[0040] A playback processing device is connected to multiple height sensors of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, respectively, for synchronously playing the current real-time height values of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device.
[0041] The playback processing device is connected to multiple height sensors of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, respectively, and is used to synchronously play the current real-time height values of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, including: the playback processing device is a character display device;
[0042] The playback processing device is connected to multiple height sensors of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, respectively, and is used to synchronously play the current real-time height values of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, including: the playback processing device is a voice playback device.
[0043] In addition, in the image content analysis and processing system utilizing location big data, a signal transmission interface is used to download remote sensing images corresponding to the location data of the scenic area coverage from remote sensing satellites for output as scenic area coverage images. The location data of the scenic area coverage includes the location information corresponding to each scenic spot in the scenic area, which is based on the BeiDou positioning mode or the Galileo positioning mode.
[0044] This invention has the following four outstanding technical effects:
[0045] First, a data judgment device is introduced, including a network learning device, a content analysis device, and an effect prediction device. The effect prediction device is connected to the network learning device and the content analysis device respectively, thereby providing hardware resources for predicting the enhancement effects of different enhancement algorithms.
[0046] Secondly: Obtain each background pixel of the combined filtered image, determine the coordinate value and brightness value of each background pixel, analyze the signal-to-noise ratio and the number of noise types in the combined filtered image, input the signal-to-noise ratio and the number of noise types in the combined filtered image, set the binary algorithm code value corresponding to the enhancement algorithm, and input the coordinate value and brightness value of each background pixel in the combined filtered image into the radial basis function neural network model and execute the radial basis function neural network model to obtain the contrast of the image after the operation, which is obtained by enhancing the combined filtered image using the set enhancement algorithm, as output by the radial basis function neural network model.
[0047] Furthermore: The radial basis function neural network model is a radial basis function neural network that has undergone multiple training actions, and the number of training actions is proportional to the number of noise types, thereby enabling the customization of the structure of the radial basis function neural network model;
[0048] Finally, by comparing the contrast values of various enhancement algorithms after passing through the radial basis function neural network model, the enhancement algorithm corresponding to the smallest contrast value among all the contrast values is selected as the preferred enhancement algorithm to perform enhancement processing on the combined filtered image, thereby obtaining the final enhanced image and thus obtaining the best content enhancement mode suitable for the content of different images.
[0049] It will be apparent to those skilled in the art that various modifications and variations can be made to this invention. Therefore, this invention is intended to cover the modifications and variations provided within the scope of the appended claims and their equivalents.
Claims
1. An image content parsing and processing system utilizing location big data, characterized in that, The system includes: A signal transmission interface is used to download remote sensing images corresponding to the location data of the scenic area coverage from remote sensing satellites based on the location data of the scenic area coverage, and output them as scenic area coverage images. The location data of the scenic area coverage includes the location information of each scenic spot in the scenic area. A content sharpening device, connected to the signal transmission interface, is used to perform spatial differential sharpening processing on the received scenic area overlay image to obtain and output the corresponding content sharpened image. A guided filtering device, connected to the content sharpening device, is used to perform guided filtering processing on the received content sharpening image to obtain and output a corresponding guided filtering image; A combined filtering device, connected to the guided filtering device, is used to perform combined filtering processing on the received guided filtering image to obtain and output the corresponding combined filtering image; A data judgment device, connected to the combined filtering device, includes a network learning device, a content analysis device, and an effect prediction device. The effect prediction device is connected to both the network learning device and the content analysis device. The data judgment device is used to acquire each background pixel of the combined filtered image, determine the coordinate value and brightness value of each background pixel, analyze the signal-to-noise ratio and the number of noise types in the combined filtered image, input the signal-to-noise ratio and the number of noise types in the combined filtered image, the binary algorithm code value corresponding to the enhancement algorithm, the coordinate value and brightness value of each background pixel in the combined filtered image into a radial basis function neural network model, and execute the radial basis function neural network model to obtain the contrast of the image after enhancement operation obtained by the combined filtered image using the set enhancement algorithm. The radial basis function neural network model is a radial basis function neural network that has undergone multiple training actions, and the number of training actions is proportional to the number of noise types. A dynamic processing device, connected to the data judgment device, is used to compare the contrast ratios corresponding to various enhancement algorithms, and select the enhancement algorithm corresponding to the smallest contrast ratio as the preferred enhancement algorithm to perform enhancement processing on the combined filtered image to obtain the final enhanced image. Among them, various enhancement algorithms include histogram equalization enhancement algorithm, logarithmic image enhancement algorithm, exponential image enhancement algorithm, and high contrast preservation enhancement algorithm.
2. The image content parsing and processing system utilizing location big data as described in claim 1, characterized in that: The signal-to-noise ratio and number of noise types of the combined filtered image, the binary algorithm code value corresponding to the enhancement algorithm, the coordinate values and brightness values of each background pixel in the combined filtered image are input into the radial basis function neural network model and the radial basis function neural network model is executed to obtain the contrast of the image after the enhancement operation of the combined filtered image by the set enhancement algorithm. The radial basis function neural network model is a radial basis function neural network that has been trained multiple times and the number of training times is proportional to the number of noise types. The process includes: using a simulation mode to complete the modeling process of the radial basis function neural network model.
3. The image content parsing and processing system utilizing location big data as described in claim 2, characterized in that, The system also includes: A height sensor is connected to the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, respectively, and is used to measure the current real-time height values of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, respectively. The height sensor is connected to the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device, respectively, and is used to measure the current real-time height values of each of the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device. The height sensor includes multiple height measurement units, each connected to the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device, to measure the current real-time height values of each of the three devices.
4. The image content parsing and processing system utilizing location big data as described in claim 3, characterized in that: The height sensor device includes multiple height measurement units, which are respectively connected to the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device to separately measure the current real-time height values of each of the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device. The multiple height measurement units are multiple height sensors, respectively connected to the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device to separately measure the current real-time height values of each of the content sharpening device, the guided filtering device, the combined filtering device, and the data judgment device.
5. The image content parsing and processing system utilizing location big data as described in claim 4, characterized in that: The plurality of height measurement units are plurality of height sensors, which are respectively connected to the content sharpening device, the guided filtering device, the combined filtering device and the data judgment device to perform separate measurements of the current real-time height values of the content sharpening device, the guided filtering device, the combined filtering device and the data judgment device, including: the plurality of height sensors have the same structure.
6. The image content parsing and processing system utilizing location big data as described in claim 5, characterized in that: The plurality of height measurement units are plurality of height sensors, which are respectively connected to the content sharpening device, the guided filtering device, the combined filtering device and the data judgment device to complete the separate measurement of the current real-time height values of the content sharpening device, the guided filtering device, the combined filtering device and the data judgment device. The plurality of height sensors have the same upper limit value and lower limit value for height measurement.
7. The image content parsing and processing system utilizing location big data as described in any one of claims 3-6, characterized in that, The system also includes: The playback processing device is connected to multiple height sensors of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, respectively, for synchronously playing the current real-time height values of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device.
8. The image content parsing and processing system utilizing location big data as described in claim 7, characterized in that: The playback processing device is connected to multiple height sensors of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, respectively, and is used to synchronously play the current real-time height values of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, including: the playback processing device is a character display device.
9. The image content parsing and processing system utilizing location big data as described in claim 7, characterized in that: A playback processing device is connected to multiple height sensors of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, respectively, for synchronously playing the current real-time height values of the content sharpening device, the guiding filter device, the combined filter device, and the data judgment device, including: the playback processing device is a voice playback device.