A method and system for detecting the radiation quality of multi-source satellite remote sensing image products

Abstract

The present invention provides a method and system for detecting the radiation quality of multi-source satellite remote sensing image products, which relates to the field of remote sensing image quality inspection. The method includes: first, obtaining multi-source satellite remote sensing images to obtain original image information. Then, performing geometric accuracy correction processing on the original image information to obtain geometric accuracy corrected image information. Next, performing atmospheric correction processing on the geometric accuracy corrected image information to obtain atmospherically corrected image information. Then, performing image enhancement processing on the atmospherically corrected image information to obtain enhanced processed image information. Finally, performing radiation quality inspection on the enhanced processed image information to obtain radiation quality inspection results. Through the method provided by the present invention, the radiation quality of multi-source satellite remote sensing image products can be accurately and effectively detected.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing image quality inspection, and more particularly, to a method and system for detecting the radiation quality of multi-source satellite remote sensing image products. Background Art

[0002] With the rapid development of remote sensing technology, many remote sensing users are deeply troubled by the problem of how to select the required data from the massive data for storage and use. Therefore, it is of great significance to conduct research on the quality evaluation of remote sensing images. The quality of remote sensing images includes multiple aspects such as radiation quality, geometric quality, and image attachment quality. Since the image is essentially the manifestation of radiation energy in two-dimensional space and is affected by various radiation biases during the remote sensing imaging process, the radiation quality detection of remote sensing images is the most important and complex.

[0003] In the prior art, the radiation quality is detected by quantitatively analyzing the similarities and differences between the remote sensing image to be detected and the reference image. Specifically, the radiation quality detection is carried out based on the modulation transfer function and information entropy. During the remote sensing imaging process, the image is affected by various radiation degradations, such as blurring, noise, sampling, quantization, clouds, invalid pixels, etc. However, the existing remote sensing image radiation quality inspection methods cannot effectively inspect them under the influence of the above radiation degradations, and there will be a large detection error, resulting in a low reliability of the final radiation quality inspection. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for detecting the radiation quality of multi-source satellite remote sensing image products, which can obtain higher-quality radiation quality inspection results by performing a series of preprocessing on the remote sensing image products and then performing corresponding radiation quality inspections.

[0005] The embodiments of the present invention are implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a method for detecting the radiation quality of multi-source satellite remote sensing image products, which includes the following steps:

[0007] Obtain multi-source satellite remote sensing images to obtain original image information;

[0008] Perform geometric accuracy correction processing on the original image information to obtain geometric accuracy corrected image information;

[0009] Perform atmospheric correction processing on the geometric accuracy corrected image information to obtain atmospheric correction processed image information;

[0010] Perform image enhancement processing on the atmospheric correction processed image information to obtain enhanced processed image information;

[0011] Perform a radiation quality inspection on the enhanced processed image information to obtain the radiation quality inspection result.

[0012] In some embodiments of the present invention, the above image enhancement processing includes at least one of histogram correction method processing, spatial domain filtering method processing, or frequency domain filtering method processing.

[0013] In some embodiments of the present invention, the above radiation quality inspection includes at least one of inter - film radiation anomaly detection, intra - film color difference anomaly detection, color cast detection, missing detection, garbled detection, or stripe noise detection.

[0014] In some embodiments of the present invention, the above inter - film radiation anomaly detection specifically includes:

[0015] Calculate the gradient values of the projections of all image layers in the enhanced processed image information respectively to obtain the image layer gradient value information of the corresponding layers;

[0016] Obtain the first gradient maximum value and the first gradient mean value in the image layer gradient value information of the corresponding layers respectively, and record the ratio of the first gradient maximum value to the first gradient mean value as r1;

[0017] Perform edge detection on the projections of all image layers in the enhanced processed image information respectively to obtain the edge detection images of the corresponding layers;

[0018] Obtain the second gradient maximum value and the second gradient mean value in the edge detection images of the corresponding layers respectively, and record the ratio of the second gradient maximum value to the second gradient mean value as r2;

[0019] Judge the size relationship between r1 and r2 of the corresponding layers respectively. If r1≥r2, it is recorded that there is no inter - film radiation anomaly problem. If r1 < r2, it is recorded that there is an inter - film radiation anomaly problem.

[0020] In some embodiments of the present invention, the above color cast detection specifically includes:

[0021] Calculate the histograms of the flatness of the two chromaticity coordinate planes a and b for each pixel point of the enhanced processed image information in the CIE lab space;

[0022] Use the formula to calculate the average chromaticity D of the image, where M and N are the width and height of the image respectively, in pixels, and a and b are the a and b components in the CIE lab space;

[0023] Use the formula to calculate the chromaticity center distance M of the image, where

[0024] Using the formula obtain the image color cast factor K, and obtain the color cast detection result according to the magnitude of the image color cast factor.

[0025] In some embodiments of the present invention, the above-mentioned missing detection specifically includes:

[0026] Obtain the double-peak histogram image information of all frames in the enhanced processed image information;

[0027] Use the adaptive threshold method to segment the double-peak histogram image information. If it is lower than the adaptive threshold, the block area is marked as a missing area;

[0028] Use the fast non-local mean denoising method to perform edge detection on the missing area to obtain the boundary information of the missing area;

[0029] Generate missing detection result information using the boundary information of the missing area.

[0030] In some embodiments of the present invention, the above-mentioned stripe noise detection specifically includes:

[0031] Periodically extract the frames to be detected in the enhanced processed image information;

[0032] Perform grayscale processing on the frames to be detected to obtain grayscale frames to be detected;

[0033] Perform Fourier transform on the grayscale frames to be detected to obtain the corresponding spectrogram;

[0034] Calculate the cumulative distribution functions in the row direction and column direction of the spectrogram, and detect whether there are abnormal peaks in the cumulative distribution functions. If so, it is recorded as the existence of stripe noise;

[0035] After enhancing the spectrogram, judge the magnitude of the frequency band amplitude in the surrounding area and the preset threshold. If the frequency band amplitude in the surrounding area is greater than the preset threshold, it is recorded as the existence of stripe noise in the surrounding area frequency band.

[0036] In a second aspect, an embodiment of the present application provides a radiation quality detection system for a multi-source satellite remote sensing image product, which includes:

[0037] An image acquisition module, configured to acquire a multi-source satellite remote sensing image to obtain original image information;

[0038] A geometric accuracy correction module, configured to perform geometric accuracy correction processing on the original image information to obtain geometric accuracy corrected image information;

[0039] An atmospheric correction module, configured to perform atmospheric correction processing on the geometric accuracy corrected image information to obtain atmospherically corrected image information;

[0040] An image enhancement module, configured to perform image enhancement processing on the image information after atmospheric correction processing to obtain enhanced processing image information;

[0041] A radiation quality module, configured to perform radiation quality inspection on the enhanced processing image information to obtain a radiation quality inspection result.

[0042] In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory for storing one or more programs; and a processor. When the above one or more programs are executed by the above processor, the method described in any one of the above first aspects is implemented.

[0043] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method described in any one of the above first aspects is implemented.

[0044] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0045] By performing a series of geometric accuracy correction processing, atmospheric correction processing, and image enhancement processing on the obtained multi-source satellite remote sensing images, remote sensing image information with higher quality and more accurate position can be obtained. Then, by performing radiation quality inspection on it, higher-quality inspection results can be obtained, thereby providing effective radiation quality inspection data support for improving the efficiency and quality of remote sensing image quality inspection. That is to say, it can effectively help non-expert remote sensing image radiation quality-related researchers accurately recognize and distinguish radiation quality problems. The method provided by the present invention not only has high inspection accuracy but also high efficiency, and can greatly expand the application depth and breadth of radiation quality inspection of remote sensing image products. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is a flowchart of an embodiment of a method for detecting the radiation quality of a multi-source satellite remote sensing image product of the present invention;

[0048] Figure 2 It is a specific flowchart of inter-slice radiation anomaly detection in an embodiment of the present invention;

[0049] Figure 3 It is a specific flowchart of color cast detection in an embodiment of the present invention;

[0050] Figure 4 This is the specific flowchart of missing detection in the embodiments of the present invention;

[0051] Figure 5 This is the specific flowchart of strip noise detection in the embodiments of the present invention;

[0052] Figure 6 This is the structural block diagram of an embodiment of a radiation quality detection system for multi-source satellite remote sensing image products of the present invention;

[0053] Figure 7 This is the structural block diagram of an electronic device provided by the embodiments of the present invention.

[0054] Icons: 1, image acquisition module; 2, geometric accuracy correction module; 3, atmospheric correction module; 4, image enhancement module; 5, radiation quality module; 6, memory; 7, processor; 8, communication interface. Detailed implementation manners

[0055] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some but not all of the embodiments of the present application. Components of the embodiments of the present application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0056] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but merely represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0057] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0058] 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. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0059] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0060] Example

[0061] See also Figure 1 The radiation quality detection method of a multi-source satellite remote sensing image product comprises the following steps:

[0062] Step S1: Acquire multi-source satellite remote sensing images to obtain original image information.

[0063] In the above steps, by acquiring multi-source satellite remote sensing images, original data support is provided for subsequent radiation quality inspection. Exemplarily, the multi-source satellite remote sensing images can be acquired in real time through corresponding satellites, or by autonomously uploading the acquired data sources.

[0064] Step S2: Perform geometric accuracy correction processing on the original image information to obtain geometric accuracy corrected image information.

[0065] In the above steps, when the remote sensing image changes in geometric position, distortions such as uneven rows and columns, inaccurate correspondence between pixel size and ground size, and irregular changes in the shape of objects are produced, it means that the remote sensing image is distorted, and geometric accuracy correction is the correction of this distortion. At the same time, geometric accuracy correction is a process of projecting image data onto a plane to make it conform to the map projection system, through which the image can be given a geographic reference. Therefore, the above processing steps can remove this distorted image, thereby providing more valuable reference data information for subsequent processing.

[0066] Exemplarily, considering the cases where there are geometric deformations in the original image information that causes acquisition can generally be divided into two categories: systematic and non-systematic. Among them, systematic ones are generally caused by the sensor itself, are regular and predictable, and can be corrected using a sensor model, that is, the errors caused by sensor characteristics and the instantaneous position, altitude, speed, rotation, line spacing, yaw, and the rotation of the earth, etc. of the remote sensor are corrected for systematic errors using the sensor model. Non-systematic geometric deformations are irregular. It can be due to the instability of the height, attitude, etc. of the sensor platform itself, or due to changes in the earth's curvature, air refraction, and terrain changes, etc. For non-systematic geometric deformations, geometric correction can be performed using ground control points (GCPs). This correction does not consider the cause of the distortion. Its essence is to approximately describe the geometric distortion process of the remote sensing image using a mathematical model, and it is considered that the overall distortion of the remote sensing image can be regarded as the combined effect of basic deformations such as squeezing, twisting, scaling, offset, and higher-order ones. The geometric distortion model is obtained using some corresponding points (i.e., control point data pairs) between the distorted remote sensing image and the standard map or image, and then this model is used for geometric distortion correction.

[0067] Step S3: Perform atmospheric correction processing on the geometric accuracy corrected image information to obtain the atmospheric correction processed image information.

[0068] In the above steps, through atmospheric correction processing, the errors caused by atmospheric scattering, absorption, and reflection in the acquired remote sensing image can be eliminated, thereby improving the subsequent data detection accuracy.

[0069] Exemplarily, when the system memory is high, the surface measurement data at the time of image transit in the three-dimensional terrain and digital image information can be obtained, and factors such as terrain undulation can be considered to correct the influence of the atmosphere and the sensor, and absolute radiometric correction can be performed on it, that is, converting the DN (Digital Number) value of the three-dimensional terrain and digital image information into the true surface reflectance. When the system memory is low, an image can be used as a reference (or benchmark) image, and then the DN value of the corresponding image in the three-dimensional terrain and digital image information can be adjusted so that the same-named terrain on the two-phase images has the same DN value, thereby realizing atmospheric correction processing. In addition, three-dimensional terrain extraction and orthophoto production processing can be performed on the geometric accuracy corrected image information. Among them, through three-dimensional terrain extraction of the geometric accuracy corrected image information, for example, artificial ground objects, vegetation, water bodies, clouds and other terrains can be extracted, and different three-dimensional reconstruction means can be adopted for different terrain types to obtain more accurate and reliable three-dimensional terrain. Among them, through orthophoto production processing of the geometric accuracy corrected image information, an orthophoto with both terrain characteristics and image characteristics can be obtained, with a larger information coverage, so that better three-dimensional terrain and digital image information can be obtained, and the accuracy of subsequent detection can be improved. Exemplarily, the orthophoto production processing process can select some ground control points on the photo of the geometric accuracy corrected image information, and use the digital elevation model already obtained within the range of this photo to correct the tilt and projection difference of the geometric accuracy corrected image information at the same time, and resample it into an orthophoto. In addition, when processing the geometric accuracy corrected image information, the low-resolution multispectral image and the high-resolution single-band image in it can also be resampled to generate an image remote sensing of a high-resolution multispectral image, so that the processed image has both a high spatial resolution and multispectral characteristics. The geometric accuracy corrected image information can also be mosaicked or cropped, that is, multiple geometric accuracy corrected image information can be spliced into one or a series of geometric accuracy corrected image information with a larger coverage area, so as to increase the coverage range in a single geometric accuracy corrected image information that can be studied. Cropping the geometric accuracy corrected image information can remove the areas outside the area to be studied, so that it can be better processed for the area to be studied. Exemplarily, it can be subframed and cropped according to the administrative division boundary or the natural division boundary.

[0070] Step S4: Perform image enhancement processing on the atmospherically corrected image information to obtain enhanced processed image information.

[0071] In the above steps, by performing image enhancement processing on the atmospherically corrected image information, the corresponding image interpretability and effect can be improved, and the visual quality of the atmospherically corrected image information can be improved or the required key features can be highlighted.

[0072] Exemplarily, the atmospherically corrected processed image information can be processed by scaling, stereoscopic effect, color enhancement, contrast enhancement (contrast stretching), edge enhancement (image sharpening), image smoothing and sharpening, image operation (ratio image), etc., according to actual requirements. For specific methods, similar algebraic operations, contrast transformation, filtering, color processing and other methods in the prior art can be adopted.

[0073] Step S5: Perform radiation quality inspection on the enhanced processed image information to obtain a radiation quality inspection result.

[0074] In the above steps, performing radiation quality inspection on the enhanced processed image information finally obtained after processing the original image information through steps S2 to S4 can achieve quantitative processing on the basis of the acquired original image information, so as to obtain image information with higher quality and more accurate position, thereby expanding the depth and breadth of radiation quality inspection. That is to say, it is possible to perform radiation quality inspection on the original image information under the influence of various radiation degradations such as blur, noise, sampling, quantization, cloud, invalid pixels, etc., reducing inspection errors and improving inspection reliability.

[0075] Please refer to Figure 1 , the above image enhancement processing includes at least one of histogram correction method processing, spatial domain filtering method processing, or frequency domain filtering method processing.

[0076] In the above steps, considering that the gray distribution of the atmospherically corrected processed image information may be concentrated in a relatively narrow range, resulting in unclear details and low contrast of the image. In order to stretch the gray level of the image to make the gray level evenly distributed, increase the contrast, and make its details clear to achieve the enhancement purpose, histogram correction method processing can be used for image enhancement, that is, changing the histogram of the given image to a uniform histogram distribution, so that the probability density of pixel gray levels is evenly distributed. In addition, edge enhancement processing of the atmospherically corrected processed image information can be effectively achieved by performing spatial domain filtering on the atmospherically corrected processed image information through tools such as smoothing operators, Laplacian operators, and gradient operators. Image enhancement processing can also be effectively achieved by performing smoothing processing on the atmospherically corrected processed image information through frequency domain filtering methods such as high-pass filtering or low-pass filtering.

[0077] Please refer to Figure 1 , the above radiation quality inspection includes at least one of inter-slice radiation anomaly detection, intra-slice color difference anomaly detection, color cast detection, missing detection, garbled detection, or stripe noise detection.

[0078] In the above steps, the radiation quality inspection mainly includes calculating characteristic indexes such as the number of 0 values, mean value, standard deviation, brightness, maximum value, and minimum value of the image. Among them, the number of 0 values refers to the large blank areas existing in many edge regions of the remote sensing image, and the number of blank areas is the number of 0 values; the mean value is calculated as the layer average value from the layer values of all n pixels that make up a remote sensing image object; the standard deviation is calculated as the standard deviation from the layer values of all n pixels that make up a remote sensing image object; the brightness is the average value of the spectral average values of a remote sensing image object; the maximum value is the maximum value obtained by sorting the layer values of all n pixels that make up a remote sensing image object; the minimum value is the minimum value obtained by sorting the layer values of all n pixels that make up a remote sensing image object. By inspecting these characteristic indexes, the radiation quality inspection results of the remote sensing image can be accurately and effectively obtained.

[0079] Please refer to Figure 2 , the above inter-chip radiation anomaly detection specifically includes:

[0080] Step S6-1: Calculate the gradient values of the projections of all image layers in the enhanced processed image information respectively, and obtain the image layer gradient value information of the corresponding layers respectively.

[0081] In the above steps, there will be color anomalies between or at the joints of the inter-chip images in the enhanced processed image information, and the number of CCD chips spliced by different satellite images is also inconsistent. There must be image joints at the intervals of all CCD chips, that is, the longitudinal stripes among them belong to edge mutations. By analyzing the gradient changes of the longitudinal projection, the stripes and their positions can be perceived. Therefore, first, by accumulating the longitudinal projections of each channel in the enhanced processed image information, the gradient values of the projections of each channel are calculated, which can provide data support for subsequent analysis of the gradient changes of the longitudinal projection.

[0082] Step S6-2: Obtain the first gradient maximum value and the first gradient mean value in the image layer gradient value information of the corresponding layers respectively, and record the ratio of the first gradient maximum value to the first gradient mean value as r1.

[0083] Step S6-3: Perform edge detection on the projections of all image layers in the enhanced processed image information respectively, and obtain the edge detection images of the corresponding layers.

[0084] In the above steps, in the above steps, through edge detection, the required boundary and shape information in the projections of all image layers in the enhanced processed image information can be identified, so as to perform analysis and calculation on it.

[0085] Exemplarily, edge detection can adopt the Canny Edge Detection algorithm. Specifically, the Canny Edge Detection algorithm includes the following steps: 1. Gaussian filtering; 2. calculating the gradient value and gradient direction; 3. filtering non-maximum values; 4. using upper and lower thresholds to detect edges. By processing all image layers through Gaussian filtering, a denoised image layer is obtained, which can make the original image smoother and may increase the width of the edges. Then, by calculating the gradient value and gradient direction of the obtained denoised image layer, the degree of change and direction of the gray value of the denoised image layer can be identified. Since the edges may be magnified during the Gaussian filtering process. Then, through the step of filtering non-maximum values, a rule is used to filter out the points that are not edges, so that the width of the edges is as close as possible to 1 pixel. Finally, by using the upper and lower thresholds to detect edges, the required boundary and contour information can be clearly defined.

[0086] Step S6-4: Obtain the second gradient maximum value and the second gradient mean value in the edge detection image of the corresponding layer respectively, and record the ratio of the second gradient maximum value to the second gradient mean value as r2.

[0087] Step S6-5: Judge the size relationship between r1 and r2 of the corresponding layer respectively. If r1≥r2, it is recorded that there is no inter-slice radiation anomaly problem. If r1<r2, it is recorded that there is an inter-slice radiation anomaly problem.

[0088] In the above steps, by judging the size relationship between r1 and r2 of the corresponding layer, it is possible to clearly understand whether there is an inter-slice radiation anomaly problem.

[0089] Please refer to Figure 3 , the above color cast detection specifically includes:

[0090] Step S6-6: Calculate the histograms of the flatness of the two chromaticity coordinate planes a and b for each pixel point of the enhanced image information in the CIE lab space.

[0091] Color cast refers to the abnormal phenomenon that the radiation energy responses of one or several bands of an image are inconsistent, resulting in the overall image showing a color tone deviation towards the band with higher radiation energy response. Color cast may occur in the satellite image shooting process due to light or angle problems. In the existing technology, the RGB color space is the simplest color space. However, the biggest limitation of the RGB color space is that when using the Euclidean distance to describe the difference between two colors, the calculated distance between the two colors cannot correctly represent the true difference between the two colors actually perceived by people. By using the CIE Lab color space, the distance between colors calculated in this space is basically consistent with the actual perception difference. Its histogram can more objectively reflect the degree of color cast of the image, so the automatic detection of color cast images under CIE Lab will be more accurate and effective.

[0092] In the above steps, through the analysis of normal images and color cast images, it is found that if in the histogram on the ab chromaticity coordinate plane, the chromaticity distribution is basically single-peaked or relatively concentrated, and the chromaticity average value is relatively large, it can be judged that there is color cast, and the larger the chromaticity average value, the more serious the color cast. Therefore, by calculating the histograms of the a and b chromaticity coordinate planes for each pixel point of the enhanced processed image information in the CIE lab space, it can provide original and effective data support for the subsequent color cast inspection.

[0093] Step S6-7: Use the formula to calculate the average chromaticity D of the image, where M and N are the width and height of the image, in pixels, and a and b are the a and b components in the CIElab space respectively.

[0094] In the above steps, by calculating d a and d b values, the average chromaticity D of the image can be calculated. When d a > 0, the image color is biased towards red, otherwise it is biased towards green; when d b > 0, the image color is biased towards yellow, otherwise it is biased towards blue.

[0095] Step S6-8: Use the formula to calculate the chromaticity center distance M of the image, where

[0096] In the above steps, by introducing the chromaticity center distance M of the image, the color cast degree of the image can be comprehensively evaluated by using it and the average chromaticity D of the image.

[0097] Step S6-9: Use the formula Obtain the image color cast factor K, and obtain the color cast detection result according to the magnitude of the image color cast factor.

[0098] In the above steps, by using the ratio of the average chromaticity D of the image to the chromaticity center distance M of the image, that is, the image color cast factor K, the degree of color cast of the image can be accurately and effectively measured.

[0099] Please refer to Figure 4 , the above missing detection specifically includes:

[0100] Step S6-10: Obtain the double-peak histogram image information of all frames in the enhanced processing image information.

[0101] In the above steps, missing means that there is no texture information in some rows and columns of the image or a certain band is missing; the radiation values of one or several bands in all or part of the image area are abnormal; during the transmission or shooting of the remote sensing image, information loss occurs in some bands, resulting in "black stripes" in the image. The pixel values of the missing part are not necessarily 0, but some points with relatively small pixel values. The image of the double-peak histogram is segmented using an adaptive threshold, and the area below the threshold is considered the missing part. Through the fast non-local means denoising method, while taking into account noise suppression and texture retention, edge detection is performed on it to detect the boundary of the block area, so that missing detection can be carried out on it. Therefore, first obtain the double-peak histogram image information of all frames in the geometric quality detection image information, so as to facilitate subsequent image segmentation processing.

[0102] Step S6-11: Segment the double-peak histogram image information using the adaptive threshold method. If it is lower than the adaptive threshold, the block area where it is located is marked as the missing area.

[0103] In the above steps, by segmenting the double-peak histogram image information using the adaptive threshold method, the missing area can be well marked out.

[0104] Exemplarily, if the gray-level histogram is significantly bimodal, the gray level corresponding to the valley bottom between the two peaks can be selected as the adaptive threshold.

[0105] Step S6-12: Use the fast non-local means denoising method to perform edge detection on the missing area to obtain the boundary information of the missing area.

[0106] In the above steps, by using the fast non-local means denoising method to perform edge detection on the missing area, that is, using the method of smoothing and taking the mean in the area around a target pixel to perform edge detection on it. Since non-local mean filtering means that it uses all the pixels in the missing area, and these pixels are weighted and averaged according to a certain similarity, therefore, the clarity of the filtered image is high and details are not lost.

[0107] Step S6-13: Generate missing detection result information using the boundary information of the missing area.

[0108] Please refer to Figure 5 , the above stripe noise detection specifically includes:

[0109] Step S6-14: Periodically extract the frames to be detected from the enhanced processed image information.

[0110] In the above steps, by periodically extracting the frames to be detected from the enhanced processed image information, the enhanced processed image information can be detected in a relatively large range. Exemplarily, if the system memory is high or the detection accuracy requirement is high, then each frame can be extracted one by one, so as to ensure that each frame can be detected and processed.

[0111] Step S6-15: Grayscale the frame to be detected to obtain a grayscale frame to be detected.

[0112] In the above steps, by grayscaling the frame to be detected, the computational amount for subsequent processing can be effectively reduced, thereby greatly improving the operation processing speed.

[0113] Step S6-16: Perform Fourier transform on the grayscale frame to be detected to obtain the corresponding spectrogram.

[0114] In the above steps, by performing Fourier transform on the grayscale frame to be detected, the data can be further dimension-reduced, thereby further improving the processing efficiency.

[0115] Step S6-17: Calculate the cumulative distribution functions in the row direction and column direction of the spectrogram, and detect whether there are abnormal peaks in the cumulative distribution functions. If so, it is recorded that there is stripe noise.

[0116] In the above steps, considering that the abnormal bright spots on the spectrogram will cause the spectral amplitude in this area to increase violently, resulting in local spectral amplitude peaks, the local peak information, that is, the abnormal bright spots among them, can be effectively detected through the cumulative distribution function.

[0117] Step S6-18: After enhancing the spectrogram, judge the magnitude relationship between the spectral amplitude of the surrounding area and the preset threshold. If the spectral amplitude of the surrounding area is greater than the preset threshold, it is recorded that there is stripe noise in the surrounding area frequency band.

[0118] In the above steps, by adjusting the size of the preset threshold, the detection accuracy of detecting whether there is stripe interference in the surrounding area frequency band can be adjusted. It should be noted that steps S6-17 and S6-18 do not strictly distinguish the order. They can be carried out simultaneously, or one step can be selected to be carried out first and the other step later, or only one of the steps can be selected to be carried out and the other step not carried out. Of course, if both steps S6-17 and S6-18 are carried out, the detection of stripe noise can be realized more accurately and the detection accuracy can be improved.

[0119] Based on the same inventive concept, please refer to Figure 6 , the present invention also provides a radiation quality detection system for multi-source satellite remote sensing image products, including:

[0120] An image acquisition module 1, configured to acquire multi-source satellite remote sensing images and obtain original image information;

[0121] A geometric accuracy correction module 2, configured to perform geometric accuracy correction processing on the original image information to obtain geometric accuracy corrected image information;

[0122] An atmospheric correction module 3, configured to perform atmospheric correction processing on three-dimensional terrain and digital image information to obtain atmospherically corrected image information;

[0123] An image enhancement module 4, configured to perform image enhancement processing on the atmospherically corrected image information to obtain enhanced processed image information;

[0124] A radiation quality module 5, configured to perform radiation quality inspection on the enhanced processed image information to obtain a radiation quality inspection result.

[0125] For the specific implementation process of the above system, please refer to a radiation quality detection method for multi-source satellite remote sensing image products provided in the embodiments of the present application, which will not be elaborated here.

[0126] Please refer to Figure 7 , Figure 7 which is a structural block diagram of an electronic device provided in an embodiment of the present invention. The electronic device includes a memory 6, a processor 7, and a communication interface 8. The memory 6, the processor 7, and the communication interface 8 are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The memory 6 can be used to store software programs and modules, such as program instructions / modules corresponding to a radiation quality detection system for multi-source satellite remote sensing image products provided in the embodiments of the present application. The processor 7 executes various functional applications and data processing by executing the software programs and modules stored in the memory 6. The communication interface 8 can be used to communicate signaling or data with other node devices.

[0127] Among them, the memory 6 can be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc.

[0128] The processor 7 can be an integrated circuit chip with signal processing capabilities. The processor 7 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0129] It can be understood that Figure 7 the structure shown is only schematic, and the electronic device may also include more or fewer components than Figure 7 those shown, or have a different configuration from Figure 7 those shown. Figure 7 Each component shown can be implemented by hardware, software, or a combination thereof.

[0130] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0131] In addition, in each embodiment of the present application, the various functional modules can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0132] If the above functions are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0133] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0134] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for detecting the radiation quality of multi-source satellite remote sensing image products, characterized in that, it includes the following steps: Obtain multi-source satellite remote sensing images to obtain original image information; Perform geometric accuracy correction processing on the original image information to obtain geometric accuracy corrected image information; Perform atmospheric correction processing on the geometric accuracy corrected image information to obtain atmospherically corrected image information; Perform image enhancement processing on the atmospherically corrected image information to obtain enhanced processed image information; Perform radiation quality inspection on the enhanced processed image information to obtain radiation quality inspection results. The radiation quality inspection includes inter-slice radiation anomaly detection, intra-slice color difference anomaly detection, color cast detection, missing detection, garbled code detection, and stripe noise detection. The inter-slice radiation anomaly detection specifically includes: calculating the gradient values of the projections of all image layers in the enhanced processed image information respectively to obtain the image layer gradient value information of the corresponding layers; Obtain the first gradient maximum value and the first gradient mean value in the image layer gradient value information of the corresponding layers respectively, and record the ratio of the first gradient maximum value to the first gradient mean value as r1; perform edge detection on the projections of all image layers in the enhanced processed image information respectively to obtain the edge detection images of the corresponding layers; obtain the second gradient maximum value and the second gradient mean value in the edge detection images of the corresponding layers respectively, and record the ratio of the second gradient maximum value to the second gradient mean value as r2; judge the size relationship between r1 and r2 of the corresponding layers respectively. If r1≥r2, it is recorded that there is no inter-slice radiation anomaly problem. If r1<r2, it is recorded that there is an inter-slice radiation anomaly problem.

2. The method for detecting the radiation quality of multi-source satellite remote sensing image products according to claim 1, characterized in that, the image enhancement processing includes at least one of histogram correction method processing, spatial domain filtering method processing, or frequency domain filtering method processing.

3. The method for detecting the radiation quality of multi-source satellite remote sensing image products according to claim 1, characterized in that, the color cast detection specifically includes: Calculating the histograms of the flatness of the two chromaticity coordinate planes a and b for each pixel point of the enhanced processed image information in the CIE lab space; Using the formula calculate the average chromaticity D of the image, where , , M and N are the width and height of the image respectively, in pixels, and a and b are the a and b components in the CIE lab color space; Using the formula the image chromaticity center distance M is calculated, where , ; Using the formula obtain the image color cast factor K, and obtain the color cast detection result according to the magnitude of the image color cast factor.

4. The method for detecting the radiation quality of multi-source satellite remote sensing image products according to claim 1, characterized in that, the missing detection specifically includes: Obtain the double-peak histogram image information of all frames in the enhanced processed image information; Use the adaptive threshold method to segment the double-peak histogram image information. If it is lower than the adaptive threshold, the block area is marked as a missing area; Use the fast non-local means denoising method to perform edge detection on the missing area to obtain the missing area boundary information; Generate missing detection result information using the missing area boundary information.

5. The method for detecting the radiation quality of multi-source satellite remote sensing image products according to claim 1, characterized in that, the stripe noise detection specifically includes: Periodically extract the frames to be detected in the enhanced processed image information; Perform grayscale processing on the frames to be detected to obtain grayscale frames to be detected; Perform Fourier transform on the grayscale frames to be detected to obtain the corresponding spectrogram; Calculate the cumulative distribution functions in the row and column directions of the spectrogram, and detect whether there are abnormal peaks in the cumulative distribution functions. If so, it is recorded as the existence of strip noise. After enhancing the spectrogram, judge the magnitude relationship between the band amplitudes in the peripheral region and a preset threshold. If the band amplitudes in the peripheral region are greater than the preset threshold, it is recorded as the existence of strip noise in the peripheral region bands.

6. A radiation quality detection system for multi-source satellite remote sensing image products Characterized in that Comprising: An image acquisition module for acquiring multi-source satellite remote sensing images to obtain original image information; A geometric accuracy correction module for performing geometric accuracy correction processing on the original image information to obtain geometric accuracy corrected image information; An atmospheric correction module for performing atmospheric correction processing on the geometric accuracy corrected image information to obtain atmospherically corrected image information; An image enhancement module for performing image enhancement processing on the atmospherically corrected image information to obtain enhanced image information; A radiation quality module for performing radiation quality inspection on the enhanced image information to obtain a radiation quality inspection result. The radiation quality inspection includes inter-slice radiation anomaly detection, intra-slice color difference anomaly detection, color cast detection, missing detection, garbled detection, and strip noise detection. The inter-slice radiation anomaly detection specifically includes: calculating the gradient values of the projections of all image layers in the enhanced image information respectively to obtain the image layer gradient value information of the corresponding layers; Obtaining the first gradient maximum value and the first gradient mean value in the image layer gradient value information of the corresponding layers respectively, and denoting the ratio of the first gradient maximum value to the first gradient mean value as r1; performing edge detection on the projections of all image layers in the enhanced image information respectively to obtain the edge detection images of the corresponding layers; obtaining the second gradient maximum value and the second gradient mean value in the edge detection images of the corresponding layers respectively, and denoting the ratio of the second gradient maximum value to the second gradient mean value as r2; judging the magnitude relationship between r1 and r2 of the corresponding layers respectively. If r1≥r2, it is recorded as no inter-slice radiation anomaly problem. If r1<r2, it is recorded as having an inter-slice radiation anomaly problem.

7. An electronic device Characterized in that Comprising: A memory for storing one or more programs; A processor; When the one or more programs are executed by the processor, the method described in any one of claims 1-5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon Characterized in that When the computer program is executed by a processor, the method described in any one of claims 1-5 is implemented.

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