A method, apparatus, and equipment for ground compensation of pixel response anomalies in high-altitude cameras.

Abstract

This invention relates to the field of high-altitude camera imaging technology, and in particular to a method, apparatus, and device for ground compensation of abnormal pixel response in high-altitude cameras. The method includes: acquiring problematic high-altitude camera images; generating a dirty image dataset; performing image incomplete processing on the dirty image dataset using an image incomplete method to obtain an incomplete image dataset; performing correction processing on the incomplete image dataset using an improved histogram matching method to obtain a corrected image dataset; removing color deviations from the corrected image dataset using spectral least squares to obtain a compensated image dataset. This improves missing spectral information, replaces stripes running through the entire image, removes gray stripes based on histogram matching, and improves image quality. This method solves problems such as invalid pixels, dead pixels, overheated pixels, contaminated pixels, and abnormal filter response in high-altitude images, thereby improving image quality and making the high-altitude camera image quality more uniform.

Description

Technical Field

[0001] This invention relates to the field of high-altitude camera imaging technology, and in particular to a method, apparatus, and equipment for ground compensation of abnormal pixel response in high-altitude cameras. Background Technology

[0002] Radiation distortion from high-altitude cameras severely impacts image applications. Researchers have focused on image radiation quality issues caused by inconsistencies in pixel response and differences in imaging instrument operation, proposing corresponding solutions. For example, for the common fringe problem, wavelet methods and L1 norm regularization can be used, and correlation can be used to remove independent column fringe noise. However, with the rapid development of high-altitude sensors, detector imaging performance has revealed more and more problems, such as invalid pixels, dead pixels, overheated pixels, contaminated pixels, and abnormal filter response. These issues bring other image quality problems besides fringes and noise, such as excessively bright or dark lines, inconsistent spectral responses, and gray-gray stripes, making image interpretation difficult and affecting the image's usability.

[0003] Due to substandard manufacturing processes in the high-altitude camera, some pixels exhibit several irreversible problems. First, there are bad pixels, which produce bright or dark stripes in the image that contrast sharply with the background. Second, there are abnormal spectral responses, leading to significant color distortion in multispectral images. Third, there is pixel contamination, which causes noticeable gray stripes in the image.

[0004] In conclusion, designing a pixel compensation method for high-altitude cameras that produces uniform image quality and high image quality is a problem that urgently needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a ground compensation method for abnormal pixel response of high-altitude cameras, so as to solve the problems of inconsistent spectral response, color distortion of spectral images, and pixel contamination in existing high-altitude camera images.

[0006] To solve the above-mentioned technical problems, the present invention provides a ground compensation method for pixel response anomalies in high-altitude cameras, comprising:

[0007] Acquire high-altitude camera images of the problem and generate a dirty image dataset;

[0008] The dirty image dataset is processed using an image missing method to obtain a missing image dataset;

[0009] The missing image dataset is then subjected to an improved histogram matching correction process to obtain a corrected image dataset.

[0010] The color deviation of the corrected image dataset is removed using the spectral least squares method to obtain the compensated image dataset.

[0011] Preferably, the step of using image missing-filling methods to perform missing-filling processing on the image dataset to obtain a missing image dataset includes:

[0012] Exploratory elements for obtaining the dirty image dataset;

[0013] The image missing data dataset is obtained by replacing the gray values ​​of the stripes in the image surrounding the problem probe with the gray values ​​of the complete image.

[0014] Preferably, the image patching calculation formula is as follows:

[0015]

[0016] in, New grayscale value for bad pixels. The gray value of the pixel to the left of the bad pixel. The gray value is the gray value of the pixel adjacent to the right of the bad pixel.

[0017] Preferably, the step of performing improved histogram matching correction processing on the missing image dataset to obtain the corrected image dataset includes:

[0018] Obtain the number of gray values ​​in the missing image dataset, and generate a cumulative histogram based on the number of gray values;

[0019] Based on the cumulative histogram, the distribution trend data of the image dataset is obtained. The gray value distribution of the anomaly detectors is pulled to near the mean using the mean and distribution factor to obtain the corrected image dataset.

[0020] Preferably, the formula for calculating the cumulative histogram is:

[0021]

[0022]

[0023]

[0024] in, This is a reference cumulative histogram based on the mean factor, where x is the statistical count of the number of probes with a DN value of j for the i-th probe, and V i,j Let ε be the mean of the statistical values ​​of all probes with DN values ​​of j. i,j λ is the adjustment factor, and λ is the empirical value.

[0025] Preferably, the step of removing the color deviation of the corrected image dataset using the spectral least squares method to obtain the compensated image dataset includes:

[0026] Obtain the spectral band loss data of the corrected image dataset;

[0027] The grayscale values ​​of the lost spectral data are linearly fitted using the multi-band least squares method to obtain a compensated image dataset.

[0028] Preferably, the linear fitting calculation formula is:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] Where n is the number of rows in the image. This is a normal blue spectrum value. This is a normal red spectrum value. This is a normal green spectrum value. The red / blue spectral average. This is the average value of the normal blue spectrum. The new grayscale value is the missing blue spectrum segment, and k is the first position where the vertical grayscale value of the image is missing.

[0035] The present invention also provides a ground compensation device for pixel response anomalies in high-altitude cameras, comprising:

[0036] The dirty image acquisition module acquires problematic high-altitude camera images and generates a dirty image dataset.

[0037] The image imputation module uses an image imputation method to imput the dirty image dataset to obtain an imputed image dataset;

[0038] The image correction module performs improved histogram matching correction processing on the missing image dataset to obtain a corrected image dataset;

[0039] The image compensation module uses the spectral least squares method to remove the color deviation of the corrected image dataset to obtain the compensated image dataset.

[0040] The present invention also provides a ground compensation device for pixel response anomalies of high-altitude cameras, comprising:

[0041] Memory, used to store computer programs;

[0042] A processor is used to implement the steps of the above-described method for ground compensation of pixel response anomalies in a high-altitude camera when executing the computer program.

[0043] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for ground compensation of pixel response anomalies in high-altitude cameras.

[0044] The present invention provides a ground compensation method for abnormal pixel response of high-altitude cameras. It uses a multi-spectral band least squares method to process the abnormal response of the filter, improves the missing spectral band information, uses an image missing method to fill in the gaps in the dirty image dataset, replaces the stripes that run through the entire image, and removes gray stripes based on histogram matching method to improve image quality. It solves the problems of invalid pixels, dead pixels, overheated pixels, contaminated pixels and abnormal filter response in high-altitude images, improves image quality and makes the image quality of high-altitude cameras more uniform. Attached Figure Description

[0045] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0046] Figure 1 A flowchart of a first specific embodiment of a ground compensation method for abnormal pixel response of a high-altitude camera provided by the present invention;

[0047] Figure 2 Image with bad pixels;

[0048] Figure 3 An image showing anomalies in the blue band response;

[0049] Figure 4 Image of dirty pixels;

[0050] Figure 5 This is an image of unfilled, corrupted pixels.

[0051] Figure 6 The image of the missing pixels after filling in the gaps;

[0052] Figure 7 This is an abnormal blue spectral response diagram;

[0053] Figure 8 To eliminate abnormal blue spectral response patterns;

[0054] Figure 9 This is a dirty pixel image;

[0055] Figure 10 The image after histogram matching;

[0056] Figure 11 This is a structural block diagram of a ground compensation device for abnormal pixel response of a high-altitude camera provided in an embodiment of the present invention. Detailed Implementation

[0057] The core of this invention is to provide a ground compensation method, device, and equipment for abnormal pixel response of high-altitude cameras. This method compensates for problems such as invalid pixels, dead pixels, overheated pixels, and lines that are too bright or too dark in the image during the imaging process of the detector, thereby improving the missing spectral information in the image and enhancing the radiometric quality of the high-altitude airborne camera image.

[0058] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Please refer to Figure 1 , Figure 1 The flowchart illustrates a first specific embodiment of a ground compensation method for pixel response anomalies in high-altitude cameras provided by the present invention; the specific operation steps are as follows:

[0060] Step S101: Obtain the high-altitude camera images of the problem and generate a dirty image dataset;

[0061] Step S102: Use image missing method to perform missing processing on the dirty image dataset to obtain a missing image dataset;

[0062] Exploratory elements for obtaining the dirty image dataset;

[0063] The image missing data dataset is obtained by replacing the gray values ​​of the stripes in the image surrounding the problem probe with the gray values ​​of the complete image.

[0064] The calculation formula for the image missing correction method is as follows:

[0065]

[0066] in, New grayscale value for bad pixels. The gray value of the pixel to the left of the bad pixel. The gray value of the pixel adjacent to the right of the bad pixel;

[0067] Step S103: Perform improved histogram matching correction processing on the missing image dataset to obtain a corrected image dataset;

[0068] Obtain the number of gray values ​​in the missing image dataset, and generate a cumulative histogram based on the number of gray values;

[0069]

[0070] Where x is the number of DN values ​​j for the i-th probe, i is the i-th probe, and S is the cumulative statistical value of gray values ​​(DN) j for the first i probes.

[0071] Based on the cumulative histogram, the distribution trend data of the image dataset is obtained. The gray value distribution of the anomaly detector is pulled to near the mean using the mean and distribution factor to obtain the corrected image dataset.

[0072] The formula for calculating the cumulative histogram is:

[0073]

[0074]

[0075]

[0076] in, This is a reference cumulative histogram based on the mean factor, where x is the statistical count of the number of probes with a DN value of j for the i-th probe, and V i,j Let ε be the mean of the statistical values ​​of all probes with DN values ​​of j. i,j λ is the adjustment factor, and λ is the empirical value.

[0077] Step S104: Use the spectral least squares method to remove the color deviation of the corrected image dataset to obtain the compensated image dataset.

[0078] Obtain the spectral band loss data of the corrected image dataset;

[0079] The gray values ​​of the lost spectral data are linearly fitted using the multi-band least squares method to obtain a compensated image dataset.

[0080] The formula for calculating the linear fit is:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] Where n is the number of rows in the image. This is a normal blue spectrum value. This is a normal red spectrum value. This is a normal green spectrum value. The red / blue spectral average. This is the average value of the normal blue spectrum. The new grayscale value is the missing blue spectrum segment, and k is the first position where the vertical grayscale value of the image is missing.

[0087] This embodiment provides a ground compensation method for pixel response anomalies in high-altitude cameras. A novel multi-band least squares method is proposed to address filter response anomalies, improving the missing spectral information. For contaminated detectors, a new histogram matching method is proposed, enhancing the radiometric quality of high-altitude airborne camera images. By using the mean plus a distribution factor, the grayscale value distribution of anomaly detectors can be brought closer to the mean, ensuring that the grayscale responses of all detectors are basically consistent. The generated lookup table corrects the image better and is more universal. By linearly fitting the grayscale values ​​at corresponding positions in other spectral bands, the grayscale values ​​of the missing blue spectral band are obtained. This solves the problem of occasional loss of partial spectral information due to environmental interference during high-altitude airborne camera imaging, which can cause partial color casts in the image, thus improving image quality and making the high-altitude camera image quality more uniform.

[0088] Based on the above embodiments, this embodiment uses specific data to describe the ground compensation method for pixel response anomalies in high-altitude cameras, as follows:

[0089] like Figure 2 , Figure 3 , Figure 4 As shown, the images were taken from an airborne high-altitude camera with a resolution of 2 meters and a size of 12000×12000. These images exhibited all the problems that could occur with the camera probe, such as multiple bad pixels in column 2072, abnormal response in the MSS blue band, and "dirty" data in columns 300-305. A total of 20 such images were selected to form test set 1.

[0090] Destriking evaluation methods use visual perception, generalized noise (GN), and white balance index to assess image quality. Visual perception refers to an acceptable image with uniform color and no stripes. Generalized noise and white balance are defined as follows:

[0091] Generalized noise is commonly used to evaluate the accuracy of image radiometric calibration. The smaller the generalized noise value, the more uniform the image quality. Specifically, an n×n uniform region image is selected. The mean of each column is calculated, and then the difference between the two (Err) is calculated. Err is GN, and the formula is:

[0092]

[0093]

[0094] The white balance algorithm assumes that the average RGB values ​​of an image are equal, i.e., ave(r) = ave(g) = ave(b). It counts the white areas of the image and then calculates the average of all colors. The maximum average is taken as the standard (MaxAve). Ave is divided by the average of each color to calculate a coefficient. The closer the sum of the coefficients is to 1, the better. The formula is as follows:

[0095] max R=max(ave(r),ave(g),ave(b))

[0096]

[0097]

[0098]

[0099] WB = AVE(R) r +R g +R b )

[0100] Among them, the closer WB is to 1, the better.

[0101] To verify that all three methods are beneficial for image restoration, we conducted a series of repeated experiments to verify their effectiveness, as follows:

[0102] For bad detector pixels, use a filling method to replace the stripes that run through the entire image, such as... Figure 5 As shown, bright line removal is performed on the dirty image dataset and on the problem image. The original gray values ​​of the bright lines are replaced with the average gray values ​​surrounding them. The processing result is as follows. Figure 6 As shown, visually, the compensated image has significantly better radiance than the original image, with uniform image quality and good overall image quality.

[0103] Table 1 shows a comparison of the radiometric correction accuracy before and after image processing using GN calculations for dirty image datasets:

[0104] Table 1 Comparison of GN before and after dataset processing.

[0105] 1 0.012 0.005 2 0.023 0.018 3 3.821 2.855 4 3.514 2.871 5 2.474 2.291 6 6.352 5.324 7 2.677 2.045 8 4.125 3.215 9 4.658 4.048 10 0.161 0.152

[0106] For anomalous responses in the blue spectral band, the missing spectral data is filled in using the least squares method, such as... Figure 7 As shown, the missing-filling method is used to eliminate image blurring or color deviation caused by abnormal blue spectral response. The processing results are as follows. Figure 8 As shown. Figure 8 Only two image processing results are listed; the other 18 images are similar. After blue spectrum compensation, the brightness of the image is significantly better than the original image, and the image quality is uniform and good.

[0107] Table 2 shows the white balance comparison before and after image processing. To reduce redundancy, only 10 WB results are shown in Table 2, as follows:

[0108] Table 2 Comparison of images before and after wb

[0109]

[0110]

[0111] As can be seen from Table 2, the WB value of the image after blue spectrum compensation is closer to 1, and the color gradation of the image is more realistic.

[0112] For gray stripes, an improved histogram matching method can be used to remove gray stripes and improve image quality, such as... Figure 9 As shown, an improved histogram matching method is used to remove gray lines caused by "dirty" pixels. Based on a lookup table, the DN values ​​of the original image are replaced with new DN values, resulting in a new image with uniform radiation. The processing result is as follows. Figure 10 As shown, Figure 10 Only two image processing results are listed; the other 18 images are similar. After relative radiometric correction, the brightness of the images is significantly better than the original images, and the image quality is uniform and good.

[0113] Table 3 shows a comparison of radiometric correction accuracy (GN) before and after image processing. To reduce redundancy, only 10 GN results are shown in Table 3, as follows:

[0114] Table 3 Comparison of Images Before and After gn

[0115]

[0116] As shown in Table 3, the radiometric correction accuracy of the processed image is significantly better than that of the original image. The improved histogram matching method is superior to the traditional histogram matching method.

[0117] This invention provides a ground compensation method for abnormal pixel response of an high-altitude camera. For bad detector pixels, a filling method is used to replace the stripes that run through the entire image. For abnormal responses in the blue spectral band, the missing spectral data is filled using the least squares method. For gray stripes, an improved histogram matching method is used to remove gray stripes, thereby improving image quality. The effectiveness of this method is verified through experimental data. The radiometric correction accuracy of the processed image is significantly better than that of the original image. The image quality is uniform and good. After blue spectral compensation, the brightness of the image is significantly better than that of the original image, thus improving the radiometric quality of the high-altitude airborne camera image and enhancing image quality.

[0118] Please refer to Figure 11 , Figure 11 A structural block diagram of a ground compensation device for pixel response anomalies in a high-altitude camera provided in an embodiment of the present invention; the specific device may include:

[0119] Dirty image acquisition module 100 acquires problematic high-altitude camera images and generates a dirty image dataset.

[0120] The image missing module 200 uses an image missing method to perform missing processing on the dirty image dataset to obtain a missing image dataset.

[0121] Image correction module 300 performs improved histogram matching correction processing on the missing image dataset to obtain a corrected image dataset;

[0122] The image compensation module 400 uses the spectral least squares method to remove the color deviation of the corrected image dataset to obtain the compensated image dataset.

[0123] This embodiment provides a ground compensation device for anomalous pixel response of a high-altitude camera, which is used to implement the aforementioned ground compensation method for anomalous pixel response of a high-altitude camera. Therefore, the specific implementation of the ground compensation device for anomalous pixel response of a high-altitude camera can be found in the embodiment section of the aforementioned ground compensation method for anomalous pixel response of a high-altitude camera. For example, the dirty image acquisition module 100, the image missing module 200, the image correction module 300, and the image compensation module 400 are respectively used to implement steps S101, S102, S103, and S104 in the aforementioned ground compensation method for anomalous pixel response of a high-altitude camera. Therefore, the specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0124] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0125] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0126] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0127] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0128] The foregoing has provided a detailed description of a ground compensation method, apparatus, and device for pixel response anomalies in high-altitude cameras provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make various improvements and modifications to this invention without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this invention.

[0129] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0130] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0131] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

Claims

1. A ground compensation method for pixel response anomalies in high-altitude cameras, characterized in that, include: Acquire high-altitude camera images of the problem and generate a dirty image dataset; The dirty image dataset is processed using an image missing method to obtain a missing image dataset; The missing image dataset is then subjected to an improved histogram matching correction process to obtain a corrected image dataset. The color deviation of the corrected image dataset is removed using the spectral least squares method to obtain the compensated image dataset. Specifically, this includes: acquiring the spectral band loss data from the corrected image dataset; and performing linear fitting processing on the grayscale values ​​of the spectral band loss data based on the multi-band least squares method to obtain the compensated image dataset. The linear fitting calculation formula is as follows: in, For the number of rows in the image, This is a normal blue spectrum value. This is a normal red spectrum value. This is a normal green spectrum value. The red / blue spectral average. This is the average value of the normal blue spectrum. To provide new grayscale values ​​for the missing blue spectrum segment. This indicates the first location where the vertical grayscale value of the image is missing.

2. The ground compensation method for pixel response anomalies in high-altitude cameras as described in claim 1, characterized in that, The method of using image missing detection to process the image dataset to obtain a missing image dataset includes: Exploratory elements for obtaining the dirty image dataset; The image missing data dataset is obtained by replacing the gray values ​​of the stripes in the image surrounding the problem probe with the gray values ​​of the complete image.

3. The ground compensation method for pixel response anomalies in high-altitude cameras as described in claim 2, characterized in that, The calculation formula for the image missing correction method is as follows: in, New grayscale value for bad pixels. The gray value of the pixel to the left of the bad pixel. The gray value is the gray value of the pixel adjacent to the right of the bad pixel.

4. The ground compensation method for pixel response anomalies of high-altitude cameras as described in claim 1, characterized in that, The improved histogram matching correction process performed on the missing image dataset yields a corrected image dataset including: Obtain the number of gray values ​​in the missing image dataset, and generate a cumulative histogram based on the number of gray values; Based on the cumulative histogram, the distribution trend data of the image dataset is obtained. The gray value distribution of the anomaly detectors is pulled to near the mean using the mean and distribution factor to obtain the corrected image dataset.

5. The ground compensation method for pixel response anomalies of high-altitude cameras as described in claim 4, characterized in that, The formula for calculating the cumulative histogram is: in, This is a reference cumulative histogram based on the mean factor. For the first The DN value of each probe is The number of statistics For all probes, the DN value is... The mean of the statistical values, As a regulating factor, These are experience points.

6. A ground compensation device for pixel response anomalies in high-altitude cameras, characterized in that, include: The dirty image acquisition module acquires problematic high-altitude camera images and generates a dirty image dataset. The image imputation module uses an image imputation method to imput the dirty image dataset to obtain an imputed image dataset; The image correction module performs improved histogram matching correction processing on the missing image dataset to obtain a corrected image dataset; The image compensation module uses spectral least squares to remove color deviations in the corrected image dataset to obtain a compensated image dataset. Specifically, this includes: acquiring the spectral band loss data from the corrected image dataset; and performing linear fitting processing on the grayscale values ​​of the spectral band loss data based on multi-band least squares to obtain the compensated image dataset. The linear fitting calculation formula is as follows: in, For the number of rows in the image, This is a normal blue spectrum value. This is a normal red spectrum value. This is a normal green spectrum value. The red / blue spectral average. This is the average value of the normal blue spectrum. To provide new grayscale values ​​for the missing blue spectrum segment. This indicates the first location where the vertical grayscale value of the image is missing.

7. A ground compensation device for pixel response anomalies in high-altitude cameras, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the ground compensation method for anomalous pixel response of an aerial camera as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the ground compensation method for anomalous pixel response of an aerial camera as described in any one of claims 1 to 5.

Images