Orthoimage mosaic method and system suitable for remote sensing image

Abstract

The invention relates to the technical field of image processing, in particular to an orthoimage mosaic method and system suitable for a remote sensing image, and the method specifically comprises the steps: analyzing the similarity of brightness values between data points in a multispectral remote sensing image, obtaining the ratio relation between different regions, and achieving the precise division of different regions in the image, the method comprises the following steps of: acquiring a low-brightness area, further acquiring a difference value among the areas, and performing targeted correction on the low-brightness area according to the acquired difference value in an image mosaic process so as to improve the overall effect of image fusion mosaic; by processing the brightness difference between the sunny area and the shady area in the orthoimage generated by processing the mountain remote sensing image, the amplitude needing to be adjusted in the shady area during HSV fusion mosaic can be further determined, so that the brightness of the shady area can be adaptively adjusted, and the image quality is improved. In combination with the high-resolution panchromatic orthoimage, a high-resolution multispectral orthoimage after brightness adjustment is obtained, and then orthoimage mosaic is carried out, so that the fusion effect of the image after mosaic is improved.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and particularly to an orthoimage mosaicking method and system applicable to remote sensing images. Background Art

[0002] Remote sensing images provide great convenience in terrain interpretation. By analyzing the spectral differences between different data points in multi-spectral remote sensing images, different terrain regions can be effectively interpreted and divided. However, in actual use, due to the large terrain undulation in mountainous areas, the brightness difference between the sunny and shady areas of mountains and peaks is significant, which may lead to unnatural phenomena during the image fusion process, thus affecting the interpretation effect of the images.

[0003] When the existing technology uses the HSV fusion method to perform fusion mosaicking analysis on multi-spectral remote sensing orthoimages in mountainous areas, there are defects that due to the large brightness difference between the shady and sunny areas of the mountains, these areas present unnatural effects in the fused images, and thus the terrain interpretation is inaccurate. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an orthoimage mosaicking method and system applicable to remote sensing images, and the specific technical solutions adopted are as follows: In the first aspect, an embodiment of this application provides an orthoimage mosaicking method applicable to remote sensing images, and this method includes the following steps: Collect each multi-spectral remote sensing image and the corresponding high-resolution panchromatic image, and perform geometric correction to obtain each multi-spectral orthoimage and high-resolution panchromatic orthoimage; For each multi-spectral orthoimage, obtain the brightness image of the multi-spectral orthoimage in the HSV color space, perform region segmentation to obtain each region in the brightness image; obtain the surrounding brightness difference of each region in the brightness image based on the brightness difference between each region and its adjacent regions; distinguish each normal region and each abnormal region in the brightness image through the surrounding brightness difference of each region; Obtain each band image of the multi-spectral orthoimage; obtain the state parameter of each region in each band image based on the spectral wavelength and the corresponding brightness value of the pixel points in each region of each band image; construct the wavelength influence coefficient of each region in each band image based on the state parameter difference between each region in each band image and its adjacent region, in combination with the surrounding brightness difference of each region; Based on the wavelength influence coefficients of each region in any two adjacent band images, in combination with the wavelength difference between the adjacent bands, construct the difference coefficient between any two adjacent band images of each region; record the mean value of all the difference coefficients of all the normal regions as the normal difference value, and construct the adjustment amplitude coefficient that needs to be adjusted for each abnormal region in combination with the difference coefficients of each abnormal region; Construct the corrected pixel values of each channel for each pixel in each abnormal region based on the amplitude coefficients of each abnormal region and the pixel values of the pixels in each band image, and combine with the high-resolution panchromatic orthoimage to obtain the high-resolution multispectral orthoimage with corrected brightness; mosaic all the high-resolution multispectral orthoimages.

[0005] In one embodiment, the expression for the surrounding brightness difference of each region in the brightness image is: , where in the formula, is the surrounding brightness difference of the th region, is the number of adjacent regions of the th region, is the average brightness value of all pixels in the th region, is the average brightness value of all pixels in the rd adjacent region of the th region, is the normalization function.

[0006] In one embodiment, the state parameter of each region in each band image is: calculate the ratio between the spectral wavelength and the corresponding brightness value of each pixel in each region, and denote it as the first ratio; take the sum value of the first ratios of all pixels in each region as the state parameter of each region.

[0007] In one embodiment, the process of obtaining the wavelength influence coefficient of each region in each band image is: In each band image, calculate the average value of the state parameters of all adjacent regions of any region; calculate the difference between the average value and the state parameter of the any region, and denote it as the first difference; take the product of the first difference and the surrounding brightness difference of the any region as the wavelength influence coefficient of the any region.

[0008] In one embodiment, the process of obtaining the difference coefficient between any two adjacent band images of each region is: Denote the ratio of the wavelength influence coefficient of each region in any band image to the wavelength influence coefficient of the region in the subsequent band image of the any band image as the second ratio; calculate the difference amount between the central wavelength of the any band and the corresponding subsequent band; take the normalized value of the product of the second ratio and the difference amount as the difference coefficient between the any band image and the corresponding subsequent band image of each region.

[0009] In one embodiment, the expression for the amplitude coefficient that needs to be adjusted for each abnormal region is: , where is the adjustment amplitude coefficient to be adjusted for the th abnormal area, is the mean value of all the difference coefficients of the th abnormal area, is the normal difference value.

[0010] In one embodiment, the process of obtaining the corrected pixel values of each channel for each pixel point in each abnormal area is as follows: Denote the corrected channel pixel value of the th pixel point in the th abnormal area as , The expression of is: where is the normalized value of the original pixel value of the th pixel point in the th abnormal area in the R-band image, is the adjustment amplitude coefficient to be adjusted for the th abnormal area; Based on the pixel values of each pixel point in the G and B band images and the amplitude coefficients of each abnormal area, respectively, using the same acquisition method as the corrected R channel pixel value, the corrected G and B channel pixel values of each pixel point in each abnormal area are obtained.

[0011] In one embodiment, the process of obtaining the high-resolution multispectral orthophoto image with corrected brightness is as follows: Based on the corrected R, G, and B channel pixel values of each pixel point in each abnormal area, perform HSV color space conversion to obtain the corrected brightness values of each pixel point in each abnormal area; Use the high-resolution panchromatic orthophoto image to replace the V channel image in the HSV image; Based on the corrected brightness value and the replaced V channel image, obtain the modified V channel image, and then obtain the modified HSV image; Transform the modified HSV image back to the RGB color space to obtain the high-resolution multispectral orthophoto image with corrected brightness.

[0012] In one embodiment, the process of obtaining the modified V channel image is as follows: For each pixel point in each abnormal area of the replaced V channel image, use the corrected brightness value of each pixel point to replace the original brightness value of each pixel point to obtain the modified V channel image.

[0013] In a second aspect, an orthophoto mosaicking system applicable to remote sensing images provided by an embodiment of the present application includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0014] The embodiments of the present application have at least the following beneficial effects: By analyzing the similarity of brightness values between data points in a multispectral remote sensing image, the present application obtains the ratio relationship between different regions, thereby achieving precise division of different regions in the image, and further obtaining the difference values between these regions. Furthermore, during the image mosaicking process, the low-brightness regions are targeted for correction according to the obtained difference values to improve the overall effect of image fusion mosaicking; in the orthophoto generated by processing mountain remote sensing images, the brightness difference between the sunny region and the shady region can be further determined to further determine the adjustment amplitude required for the shady region during HSV fusion mosaicking, so that the brightness of the shady region can be adaptively adjusted, and a high-resolution multispectral orthophoto with adjusted brightness is obtained by combining with a high-resolution panchromatic orthophoto, and then orthophoto mosaicking is performed to improve the fusion effect of the mosaicked image. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of the steps of an orthophoto mosaicking method applicable to remote sensing images provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the process for obtaining a high-resolution multispectral orthophoto with corrected brightness. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of the orthophoto mosaicking method and system applicable to remote sensing images proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs.

[0019] The following specifically describes the specific solutions of the orthophoto mosaic method and system applicable to remote sensing images provided by this application in conjunction with the accompanying drawings.

[0020] Please refer to Figure 1 , which shows a flowchart of the steps of the orthophoto mosaic method applicable to remote sensing images provided by an embodiment of this application. The method includes the following steps: Step S1, collect each multispectral remote sensing image and the corresponding high-resolution panchromatic image, and perform geometric correction to obtain each multispectral orthophoto and high-resolution panchromatic orthophoto.

[0021] Collect the multispectral remote sensing images of the mountainous area through a drone equipped with a multispectral imager. At the same time, collect the high-resolution panchromatic images of the mountainous area through a satellite sensor. Preferably, in the embodiment of this application, two multispectral remote sensing images with an overlapping area are used as the multispectral remote sensing images to be mosaicked. As other embodiments of this application, the implementer can set the multispectral remote sensing images to be mosaicked according to the actual situation.

[0022] Due to the influence of the shooting angle and terrain undulation, there are inclination phenomena in the mountains, peaks, etc. in the collected images. Therefore, the collected multispectral remote sensing images and high-resolution panchromatic images are geometrically corrected through the RPC (Rational Polynomial Coefficients) model to obtain multispectral orthophotos and high-resolution panchromatic orthophotos. Among them, the RPC model is a well-known technology, and the specific process will not be elaborated here.

[0023] It should be noted that for the acquisition of orthophotos, this application only provides a remote sensing image correction method. There are many existing remote sensing image correction methods, and the implementer can also use other remote sensing image correction models to obtain multispectral orthophotos and high-resolution panchromatic orthophotos. This application does not make specific restrictions.

[0024] Step S2, for each multispectral orthophoto, obtain the brightness image of the multispectral orthophoto in the HSV color space, perform region segmentation to obtain each region in the brightness image; obtain the surrounding brightness difference of each region in the brightness image based on the brightness difference between each region and its adjacent regions; distinguish each normal region and each abnormal region in the brightness image through the surrounding brightness difference of each region.

[0025] Taking any multispectral orthophoto as an example, the following analysis is carried out: (1) Obtain the brightness image of the multispectral orthophoto: Convert the multispectral orthophoto to the HSV color space, and use the V-channel image in the HSV color space as the luminance image of the multispectral orthophoto. Among them, the conversion of the HSV color space is a well-known technology, and the specific process will not be elaborated here.

[0026] It should be noted that for the acquisition of the luminance image of the orthophoto, this application only provides a method for obtaining the luminance image. There are many existing methods for obtaining the luminance image, and implementers can also use other luminance conversion methods to obtain the luminance image of the orthophoto. This application does not make specific restrictions.

[0027] (2) Obtain each region in the luminance image of the multispectral orthophoto: Perform contour detection on the luminance image of the multispectral orthophoto, use the number of regions detected in the luminance image as the number of initial seed points for the region growing algorithm, and further use the luminance image as the input of the region growing algorithm. The output is each region in the luminance image, realizing the region segmentation of the luminance image.

[0028] It should be noted that for the region segmentation of the luminance image, this application only provides a region segmentation method. There are many existing region segmentation methods, and implementers can also use other region segmentation algorithms to perform region segmentation on the luminance image. This application does not make specific restrictions.

[0029] (3) Obtain the surrounding luminance differences of each region in the luminance image of the multispectral orthophoto: Due to the obstruction of mountains, the shady areas and valleys in mountainous areas receive less sunlight, resulting in these terrain areas appearing darker. In the images obtained through aerial data, the luminance of these areas is significantly lower than that of adjacent areas. Therefore, for the luminance image of the multispectral orthophoto, based on the differences between the luminance of each region in the luminance image and the luminance of each of its adjacent regions, construct the surrounding luminance differences of each region in the luminance image. The expression is: , where is the surrounding luminance difference of the th region, is the number of adjacent regions of the th region, is the average luminance of all pixel points in the th region, is the average luminance of all pixel points in the th adjacent region of the th region, is the normalization function.

[0030] The greater the brightness difference between each region and its adjacent regions, and at the same time, the lower the brightness of each region, the more likely it is that the region is a shaded area such as a valley where the sunlight intensity is low, and the more necessary it is to adjust the brightness of the region.

[0031] (4)Set a brightness difference threshold. Preferably, in the embodiment of the present application, the brightness difference threshold is set to 0.6. As other embodiments of the present application, the implementer can set the brightness difference threshold according to the actual situation. If any region in the brightness image is greater than or equal to the brightness difference threshold, then the any region is regarded as an abnormal region, and the abnormal region and all its adjacent regions are combined to obtain an abnormal region group, where the abnormal region is recorded as the central abnormal region of its abnormal region group. Similarly, if any region in the brightness image is less than the brightness difference threshold, then the any region is regarded as a normal region, and the normal region and all its adjacent regions are combined to obtain a normal region group, where the normal region is recorded as the central normal region of its normal region group.

[0032] Each abnormal region and each normal region of the multi-spectral orthophoto image are obtained by the above method.

[0033] Step S3, obtain the images of each band of the multi-spectral orthophoto image; obtain the state parameters of each region in each band image based on the spectral wavelength and the corresponding brightness value of the pixel points in each region of each band image; based on the difference in the state parameters between each region and its adjacent regions in each band image, and combined with the surrounding brightness difference of each region, construct the wavelength influence coefficient of each region in each band image.

[0034] (1)For the multi-spectral orthophoto image, obtain the image of each band of the multi-spectral orthophoto image. Each band of the multi-spectral orthophoto image includes blue (B), green (G), red (R), red edge (RE), and near-infrared (NIR) bands.

[0035] (2)When conducting multi-spectral photography of mountainous areas, due to the changes in the incident angle and illumination intensity of sunlight, the shaded side of the mountain range receives less sunlight, resulting in a decrease in the spectral reflectance of the ground objects in this area, and further causing the shaded area in the multi-spectral remote sensing image to present a darker tone. This kind of phenomenon will occur in the spectral images of different bands. Therefore, to obtain the influence degree of the shadow area on the spectral information of different bands, in any band image of the multi-spectral orthophoto image, for any abnormal region group, based on the difference between the spectral wavelength and brightness of each region in the abnormal region group, construct the state parameter of each region in the abnormal region group, and the expression is: ; In the formula, is the state parameter of the th region in the abnormal region group, is the number of pixel points in the th region in the abnormal region group, is the th region in the abnormal region group, and is the spectral wavelength of the th pixel point in the th region. is the brightness value corresponding to the th pixel point in the brightness image. Among them, is the first ratio, indicating the relationship between brightness and overall parameters.

[0036] Furthermore, to analyze the actual influence of the wavelength by terrain changes, for any abnormal region group in the any-band image, based on the state parameters of each region in the abnormal region group, combined with the surrounding brightness differences of the central abnormal region of the abnormal region group, a wavelength influence coefficient of the central abnormal region of the abnormal region group is constructed, and the expression is: ; In the formula, is the wavelength influence coefficient of the central abnormal region of the abnormal region group, is the surrounding brightness difference of the central abnormal region of the abnormal region group, is the number of adjacent regions of the central abnormal region of the abnormal region group, is the state parameter of the th adjacent region of the central abnormal region of the abnormal region group, is the state parameter of the central abnormal region of the abnormal region group, is the normalization function. Among them, is the first difference.

[0037] The first difference is the difference between the state parameter of the abnormal region and the state parameter of its adjacent region, indicating the degree of spectral influence of the shadow on different wavelengths.

[0038] Through the above method, the wavelength influence coefficients of each abnormal region in any-band image of the multispectral remote sensing image can be obtained.

[0039] Step S4: Based on the wavelength influence coefficients of each region in any two adjacent band images, combined with the wavelength difference between the adjacent bands, construct a difference coefficient between any two adjacent band images of each region; record the mean value of all the difference coefficients of all normal regions as the normal difference value, and combined with the difference coefficients of each abnormal region, construct an adjustment amplitude coefficient that needs to be adjusted for each abnormal region.

[0040] (1) The degree of influence on different wavelengths may vary. It is necessary to analyze the influence on the same area at different wavelengths to obtain the degree of influence of different wavelengths on the spectral image. When obtaining the multi-spectral remote sensing image of undulating mountains, spectral images of multiple bands are collected for the same terrain area, and the spectral images of multiple bands are synthesized. During the synthesis process, it is necessary to analyze the smoothness of the fusion. If the spectral images of different bands in the fused multi-spectral remote sensing image can be smoothly fused and the pixels are clear, the fusion standard is met; conversely, if the resolution of the fused image is low and blurred, adjustment is required. Therefore, when fusing the spectral images of multiple bands, it is necessary to compare the spectral splicing smoothness between the spectral images of different bands.

[0041] For any two adjacent band images of the multi-spectral remote sensing image, based on the difference between the wavelength influence coefficients of each abnormal area in the images of these two adjacent bands, combined with the wavelength difference between these two adjacent bands, a difference coefficient of each abnormal area between the images of these two adjacent bands is constructed. The expression is: , where is the difference coefficient of the th abnormal area of the multi-spectral remote sensing image between the th and the th band images, , are respectively the wavelength influence coefficients of the th abnormal area in the th and the th band images, is the difference in the central wavelengths between the th and the th bands, is the sigmoid function. Among them, is the second ratio.

[0042] The larger it is, the greater the degree of influence of the adjacent bands by the shadow, and vice versa; The larger it is, the greater the actual difference between the two spectra.

[0043] (2) When synthesizing the multi-spectral remote sensing image from the spectral images of multiple bands, relying solely on a fixed threshold or a single fixed value cannot accurately reflect the specific degree of influence of different bands by the shadow. Therefore, first, based on the spectral wavelengths and corresponding brightness values of all pixel points in each normal area group in all band images, and the brightness difference around the central normal area, using the same acquisition method as the difference coefficient of each abnormal area between any two adjacent band images, the difference coefficient of each normal area between any two adjacent band images is obtained.

[0044] Then, calculate the mean value of the difference coefficients between all normal regions in all adjacent band images, which is denoted as the normal difference value.

[0045] After that, based on the difference coefficients between any two adjacent band images of each abnormal region and in combination with the normal difference value, construct the adjustment amplitude coefficient that needs to be adjusted for each abnormal region. The expression is: , where in the formula, is the adjustment amplitude coefficient that needs to be adjusted for the th abnormal region, is the mean value of all the difference coefficients of the th abnormal region, is the normal difference value.

[0046] Step S5: Based on the amplitude coefficients of each abnormal region and the pixel values of the pixel points in each band image, construct the corrected pixel values of each channel for each pixel point in each abnormal region. In combination with the high-resolution panchromatic orthophoto, obtain the high-resolution multispectral orthophoto with brightness correction; mosaic all the high-resolution multispectral orthophotos.

[0047] (1) Usually, the R, G, and B band images of the multispectral remote sensing image are superimposed so that the visualized color is closer to the RGB image of the actual scene. Therefore, for the R band image of the multispectral image, based on the pixel values of each pixel point in the R band image and the adjustment amplitude coefficient that needs to be adjusted for each abnormal region, construct the corrected pixel values of each pixel point in each abnormal region of the R band image. The expression is: , where in the formula, is the corrected R channel pixel value of the th pixel point in the th abnormal region, is the normalized value of the original pixel value of the th pixel point in the th abnormal region in the R band image, is the adjustment amplitude coefficient that needs to be adjusted for the th abnormal region.

[0048] Based on the pixel values of each pixel point in the G and B band images and the adjustment amplitude coefficient that needs to be adjusted for each abnormal region respectively, and using the same acquisition method as the corrected R channel pixel value, obtain the corrected G and B channel pixel values of each pixel point in each abnormal region.

[0049] (2)Perform HSV color space conversion based on the corrected R, G, and B channel pixel values of each pixel in each abnormal region to obtain the corrected brightness values of each pixel in each abnormal region. Among them, obtaining the V value of each pixel in the HSV space based on the RGB values of each pixel is a well-known technology, and the specific process will not be elaborated here.

[0050] (3)For the HSV image of the multispectral remote sensing image, first replace the V-channel image in the HSV image with the high-resolution panchromatic orthoimage, and then for each pixel in each abnormal region of the replaced V-channel image, use the corrected brightness value of each pixel to replace the original brightness value of each pixel to obtain the modified V-channel image, and further obtain the modified HSV image.

[0051] Furthermore, transform the modified HSV image back to the RGB color space, and the obtained RGB image is the high-resolution multispectral orthoimage with corrected brightness. The RGB image obtained at this time contains both the spectral information of the original multispectral remote sensing image and the spatial resolution of the high-resolution panchromatic image.

[0052] (4)Perform the above processing on each multispectral remote sensing image to be mosaicked to obtain each high-resolution multispectral orthoimage, and mosaic all the high-resolution multispectral orthoimages to form a larger-scale mosaic image to improve the correctness of the analysis results and complete the mosaic of multiple remote sensing images. Among them, mosaicking the high-resolution multispectral orthoimages is a well-known technology, and the specific process will not be elaborated here.

[0053] The schematic diagram of the process for obtaining the high-resolution multispectral orthoimage with corrected brightness is as Figure 2 shown.

[0054] Based on the same inventive concept as the above method, the embodiment of the present application also provides an orthoimage mosaic system applicable to remote sensing images, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods for the orthoimage mosaic method applicable to remote sensing images.

[0055] In summary, the embodiment of the present application provides an orthophoto mosaic method applicable to remote sensing images. By analyzing the similarity of brightness values between data points in multi-spectral remote sensing images, the ratio relationship between different regions is obtained, so as to achieve accurate division of different regions in the image, and further obtain the difference values between these regions. Furthermore, during the image mosaic process, the low-brightness regions are corrected specifically according to the obtained difference values to improve the overall effect of image fusion and mosaic. In the orthophoto generated by processing mountain remote sensing images, the brightness difference between the sunny region and the shady region can be further determined to further determine the adjustment amplitude required for the shady region during HSV fusion and mosaic, so as to be able to adaptively adjust the brightness of the shady region. Combining with the high-resolution panchromatic orthophoto, a high-resolution multi-spectral orthophoto with adjusted brightness is obtained, and then orthophoto mosaic is performed to improve the fusion effect of the mosaicked image.

[0056] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the specific embodiments of the present application are described above. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multi-task processing and parallel processing are also possible or may be advantageous.

[0057] Each embodiment in the present application is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0058] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. An orthophoto mosaic method applicable to remote sensing images, characterized in that: The method comprises the following steps: Collect various multispectral remote sensing images and corresponding high-resolution panchromatic images, and perform geometric correction to obtain various multispectral orthophotos and high-resolution panchromatic orthophotos; For each multispectral orthophoto, a brightness image of the multispectral orthophoto in the HSV color space is obtained, and regional segmentation is performed to obtain each region in the brightness image; based on the brightness difference between each region and the adjacent region, the surrounding brightness difference of each region in the brightness image is obtained; and the normal regions and abnormal regions in the brightness image are distinguished by the surrounding brightness difference of each region; Obtain each band image of the multispectral orthophoto; obtain the state parameters of each area in each band image based on the spectral wavelength and corresponding brightness value of the pixel points in each area in each band image; construct the wavelength influence coefficient of each area in each band image based on the difference in state parameters between each area in each band image and its adjacent areas, combined with the surrounding brightness difference of each area; Based on the wavelength influence coefficient of each region in any two adjacent band images, combined with the wavelength difference of the adjacent bands, the difference coefficient of each region between any two adjacent band images is constructed; the average of all the difference coefficients of all normal regions is recorded as the normal difference value, and combined with the difference coefficients of each abnormal region, the amplitude coefficient that needs to be adjusted for each abnormal region is constructed; Based on the amplitude coefficient of each abnormal area and the pixel value of each pixel in each band image, the corrected pixel value of each channel of each pixel in each abnormal area is constructed, and combined with the high-resolution panchromatic orthophoto, the high-resolution multispectral orthophoto with brightness correction is obtained; all high-resolution multispectral orthophotos are mosaicked.

2. The orthophoto mosaic method applicable to remote sensing images as claimed in claim 1, characterized in that: The expression of the surrounding brightness difference of each area in the brightness image is: , where For the The surrounding brightness difference of each area, For the The number of adjacent regions of a region, For the The average brightness of all pixels in the region, For the Region The average brightness of all pixels in the adjacent area, is the normalization function.

3. The orthophoto mosaic method applicable to remote sensing images according to claim 1, characterized in that: The state parameter of each area in each band image is as follows: the ratio between the spectral wavelength of each pixel point in each area and the corresponding brightness value is calculated, recorded as the first ratio; and the sum of the first ratios of all pixels in each area is taken as the state parameter of each area.

4. The orthophoto mosaic method applicable to remote sensing images according to claim 1, characterized in that: The process of obtaining the wavelength influence coefficient of each area in each band image is as follows: In each band image, the mean of the state parameters of all adjacent areas of any area is calculated; the difference between the mean and the state parameter of any area is calculated and recorded as the first difference; the product of the first difference and the surrounding brightness difference of any area is taken as the wavelength influence coefficient of any area.

5. The orthophoto mosaic method applicable to remote sensing images according to claim 1, characterized in that: The process of obtaining the difference coefficient between any two adjacent band images of each region is as follows: The ratio of the wavelength influence coefficient of each region in any band image to the wavelength influence coefficient in the subsequent band image of the any band image is recorded as the second ratio; the difference between the central wavelengths of the any band and the corresponding subsequent band is calculated; and the normalized value of the product of the second ratio and the difference is used as the difference coefficient of each region between the any band image and the corresponding subsequent band image.

6. The orthophoto mosaic method applicable to remote sensing images according to claim 1, characterized in that: The expression of the amplitude coefficient that needs to be adjusted in each abnormal area is: , where For the The amplitude coefficient of the abnormal area that needs to be adjusted, For the The mean of all the coefficients of variation in the abnormal regions, is the normal difference value.

7. The orthophoto mosaic method applicable to remote sensing images according to claim 1, characterized in that: The process of obtaining the corrected pixel values ​​of each channel of each pixel point in each abnormal area is as follows: The first In the abnormal area The corrected channel pixel value of the pixel is recorded as , The expression is: , where The R band image In the abnormal area The normalized value of the original pixel value of pixels, For the The amplitude coefficient of each abnormal area that needs to be adjusted; Based on the pixel value of each pixel point in the G and B band images and the amplitude coefficient of each abnormal area, the corrected G and B channel pixel values ​​of each pixel point in each abnormal area are obtained in the same acquisition method as the corrected R channel pixel value.

8. The orthophoto mosaic method applicable to remote sensing images according to claim 1, characterized in that: The process of obtaining the brightness-corrected high-resolution multispectral orthophoto is as follows: Based on the corrected R, G, and B channel pixel values ​​of each pixel in each abnormal area, HSV color space conversion is performed to obtain the corrected brightness value of each pixel in each abnormal area; Replace the V channel image in the HSV image with a high-resolution panchromatic orthophoto; A modified V channel image is obtained based on the corrected brightness value and the replaced V channel image, and then a modified HSV image is obtained; the modified HSV image is transformed back into the RGB color space to obtain a high-resolution multispectral orthophoto with corrected brightness.

9. The orthophoto mosaic method applicable to remote sensing images as claimed in claim 8, characterized in that: The modified V channel image acquisition process is: for each pixel point in each abnormal area in the replaced V channel image, the original brightness value of each pixel point is replaced by the corrected brightness value of each pixel point to obtain the modified V channel image.

10. An orthophoto mosaic system for remote sensing images, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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