A remote sensing image low-frequency noise correction method and system

Abstract

The application relates to a remote sensing image low-frequency noise correction method and system, relates to the field of remote sensing image processing, and realizes accurate removal of stripe noise under the condition of reducing satellite image detail loss, and solves the problems of unstable correction effect, easy loss of image details and difficulty in accurately describing prior features of existing remote sensing image low-frequency noise correction technologies. The remote sensing image low-frequency noise correction process takes column mean value or row mean value as a basic processing unit; fine noise intensity calibration is carried out; the image low-frequency noise is corrected in a mean value compensation mode, so that the image detail loss after correction is avoided. The method is suitable for multispectral and panchromatic remote sensing images of remote sensing satellites and is not affected by the types of ground objects.

Description

Technical Field

[0001] This invention relates to the field of remote sensing image processing, and more specifically to the field of noise correction for remote sensing satellite imagery. Background Technology

[0002] Remote sensing satellites capture surface reflection and radiation information through sensors, which is then analyzed and transformed into visualized image data to provide geographic information support for various industries. However, the performance limitations of the imaging system of the satellite common application support platform and interference from the external environment during the imaging process cause differences in the response of different sensors to the same radiation energy during scanning and imaging. This results in gradual low-frequency stripe noise in the remote sensing images, affecting the visual effect and subsequent image processing.

[0003] Traditional methods for low-frequency noise correction in remote sensing images can be mainly divided into the following three categories:

[0004] The first category consists of methods based on image statistical features. Examples include moment matching and histogram matching. These methods are highly dependent on scene uniformity, and the correction effect is unstable when the response distribution in different regions of the scene is uneven.

[0005] The second category is filtering-based methods. Examples include spatial domain filtering and frequency domain filtering. While these methods are effective in handling periodic noise, they often fail to accurately distinguish between ground features and noise characteristics when dealing with low-frequency noise. This leads to the misfiltering of useful information and the loss of image details during denoising.

[0006] The third category is based on optimization correction techniques. These methods recover the original information from degraded images using prior knowledge such as sparsity and low rank. However, due to the complex characteristics of low-frequency noise, prior features are difficult to accurately characterize, which can easily lead to under-correction or over-correction problems in the noise correction process.

[0007] In summary, existing methods for low-frequency noise correction in remote sensing images suffer from problems such as unstable correction results, easy loss of image details, and difficulty in accurately characterizing prior features. Summary of the Invention

[0008] This invention achieves precise removal of stripe noise while minimizing the loss of detail in satellite images, alleviating the problems of unstable correction effects, easy loss of image details, and difficulty in accurately characterizing prior features in existing low-frequency noise correction techniques for remote sensing images. This invention provides the following solution:

[0009] Option 1: A method for correcting low-frequency noise in remote sensing images, wherein the correction method is as follows:

[0010] Step S1: Acquire the remote sensing image to be corrected by:

[0011]

[0012] The first image of the remote sensing image to be corrected was obtained. column vectors mean ,in, , The column number of the remote sensing image to be corrected. The row number of the remote sensing image to be corrected. The first of the remote sensing images to be corrected line, number The pixel value of the column pixel;

[0013] Step S2, based on the first of the remote sensing images to be corrected column vectors mean ,pass:

[0014]

[0015] Obtain the reference value of the remote sensing image to be corrected. ;

[0016] pass:

[0017]

[0018] Obtain the column number of the reference column of the remote sensing image to be corrected. , the column number The corresponding column vector is used as the reference column, and the mean of the reference column is used as the original low-frequency noise intensity. ;

[0019] Step S3: Calibrate the low-frequency noise intensity based on the reference column. ;

[0020] Step S4: Using the reference column as a benchmark, obtain the mean adjustment value of the remote sensing image to be corrected. ;

[0021] Step S5: Utilize the low-frequency noise intensity obtained in step S3. and the mean adjustment value obtained in step S4 Perform pixel correction within column vectors, by

[0022]

[0023] Obtain the pixel values ​​in the corrected remote sensing image. Complete the correction of the remote sensing image to be corrected.

[0024] Furthermore, in one embodiment of the present invention, step S3 specifically comprises:

[0025] Step S31, based on the reference column number and the corresponding original low-frequency noise intensity ,pass

[0026]

[0027] Obtain the column number of the reference column of the remote sensing image to be corrected. excluding the first The raw low-frequency noise intensity of the column vector of the column ;

[0028] Step S32: Use the SG filter method to adjust the original low-frequency noise intensity. Filtering is performed to obtain the low-frequency noise intensity. .

[0029] Furthermore, in one embodiment of the present invention, step S32 specifically involves:

[0030] Step S321, through

[0031]

[0032] Get the length of the sliding window ,in It is a positive integer;

[0033] Step S322: For the mean of the remote sensing image column to be corrected within the sliding window, perform polynomial fitting using the least squares method, where the polynomial degree is n, to obtain the smoothing coefficient. ;

[0034] Step S323, through

[0035]

[0036] Obtain low frequency noise intensity ,in, The mean value of the remote sensing image column to be corrected within the sliding window;

[0037] Step S324: Repeat steps S321 to S323 by moving the sliding window until the sliding window has moved through all columns of the remote sensing image to be corrected, and finally obtain the low-frequency noise intensity. .

[0038] Furthermore, in one embodiment of the present invention, step S4 specifically comprises the following steps:

[0039] Step S41, based on the first step described in step S1 Column vector mean and the low-frequency noise intensity described in step S3 ,pass

[0040]

[0041] Obtain the ideal column vector mean ;

[0042] Step S42, through

[0043]

[0044] Obtain the column mean of the remote sensing image image to the left of the reference column. ;

[0045] Step S43, through

[0046]

[0047] Obtain the column mean of the remote sensing image image to the right of the reference column. ;

[0048] Step S44, through

[0049]

[0050] Obtain the mean of the left and right images ;

[0051] Step S45, through

[0052]

[0053] Obtain the mean-adjusted value .

[0054] Furthermore, in one embodiment of the invention, row vectors are used instead of column vectors.

[0055] Option 2: A low-frequency noise correction system for remote sensing images, comprising the following modules:

[0056] Module 1 is used to acquire the remote sensing image to be corrected, through:

[0057]

[0058] The first image of the remote sensing image to be corrected was obtained. column vectors mean ,in, , The column number of the remote sensing image to be corrected. The row number of the remote sensing image to be corrected. The first of the remote sensing images to be corrected line, number The pixel value of the column pixel;

[0059] Module 2, used for the first... column vectors mean ,pass:

[0060]

[0061] Obtain the reference value of the remote sensing image to be corrected. ;

[0062] pass:

[0063]

[0064] Obtain the column number of the reference column of the remote sensing image to be corrected. , the column number The corresponding column vector is used as the reference column, and the mean of the reference column is used as the original low-frequency noise intensity. ;

[0065] Module 3 is used to calibrate the low-frequency noise intensity based on the reference column. ;

[0066] Module four is used to obtain the mean adjustment value of the remote sensing image to be corrected based on the reference column. ;

[0067] Module 5 is used to utilize the low-frequency noise intensity obtained from Module 3. The mean adjustment value obtained from Module 4 Perform pixel correction within column vectors, by

[0068]

[0069] Obtain the pixel values ​​in the corrected remote sensing image. Complete the correction of the remote sensing image to be corrected.

[0070] Option 3: An electronic device according to the present invention includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0071] Memory, used to store computer programs;

[0072] When a processor executes a program stored in memory, it implements any of the remote sensing image low-frequency noise correction methods described above.

[0073] Option 4: A computer-readable storage medium according to the present invention, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements any of the remote sensing image low-frequency noise correction methods described above.

[0074] The remote sensing image low-frequency noise correction method and system described in this invention is a highly stable and lightweight remote sensing satellite image low-frequency noise correction method and system based on multiple payloads, oriented towards a common application support platform for remote sensing satellites. It can achieve accurate removal of stripe noise while minimizing the loss of satellite image details, alleviating the problems of unstable correction effects, easy loss of image details, and difficulty in accurately characterizing prior features in existing remote sensing image low-frequency noise correction techniques. This improves the appearance quality and usability of image data, providing more reliable information support for various fields. Specific beneficial effects include:

[0075] 1. The low-frequency noise correction method for remote sensing images described in this invention is used to correct low-frequency noise in remote sensing images for common applications of remote sensing satellites. The difference between this method and existing technologies lies in the limitations of traditional methods, which rely on prior estimation, resulting in unstable correction effects, loss of image details, and difficulty in accurately characterizing prior features. To address these issues, this invention employs a novel design concept. The low-frequency noise correction process uses column or row mean values ​​as basic processing units, preserving image resolution and reducing computational complexity. Through refined noise intensity calibration, it adaptively achieves radiometric correction of remote sensing images under complex noise characteristics. Low-frequency noise is corrected using mean compensation, avoiding loss of detail in the corrected image. Based on this concept, the correction method significantly reduces processing time and resource consumption, improves the stability of correction effects, and exhibits good applicability and robustness. It can handle low-frequency noise in remote sensing impact data obtained from different types of satellites, demonstrating strong versatility.

[0076] 2. In the method described in this invention, the noise intensity calibration is achieved by calculating the original low-frequency noise intensity, and then the SG filter method is used to filter the original low-frequency noise intensity to eliminate the influence of individual outliers, effectively improving the quality of the corrected remote sensing image, avoiding overcorrection during radiometric correction, and adaptively realizing radiometric correction of remote sensing images under complex noise characteristics.

[0077] 3. In the correction method described in this invention, the mean compensation method is to compensate the mean of the column vector according to the low-frequency noise intensity value of each column to obtain the ideal mean of the column vector of the corrected image; by calculating the mean of the left and right images, the color of the left and right images of the corrected reference column is processed at one time, maintaining the color tone consistency; by obtaining the mean adjustment value of the remote sensing image to be corrected, the mean compensation of the left and right images of the reference column is performed column by column, avoiding the loss of detail in the corrected image.

[0078] The method described in this invention is applicable to multispectral and panchromatic remote sensing images from remote sensing satellites and is not affected by the type of ground cover. Attached Figure Description

[0079] 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:

[0080] Figure 1 This is a flowchart illustrating the low-frequency noise correction method for remote sensing images described in Implementation Method 1.

[0081] Figure 2 The above are example images comparing multispectral remote sensing images as described in Embodiment 1, wherein (a) is the multispectral remote sensing image before low-frequency stripe noise correction, and (b) is the multispectral remote sensing image after correction using the low-frequency stripe noise correction method described in this invention.

[0082] Figure 3 These are example images comparing panchromatic remote sensing images as described in Embodiment 1, wherein (a) is the panchromatic remote sensing image before low-frequency stripe noise correction, and (b) is the panchromatic remote sensing image after correction using the low-frequency stripe noise correction method described in this invention. Detailed Implementation

[0083] Various embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. The embodiments described with reference to the drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0084] Implementation Method 1: The low-frequency noise correction method for remote sensing images described in this implementation method, such as... Figure 1 As shown, the correction method is as follows:

[0085] Step S1: Acquire the remote sensing image to be corrected by:

[0086]

[0087] The first image of the remote sensing image to be corrected was obtained. column vectors mean ,in, , The column number of the remote sensing image to be corrected. The row number of the remote sensing image to be corrected. The first of the remote sensing images to be corrected line, number The pixel value of the column pixel;

[0088] Step S2, based on the first of the remote sensing images to be corrected column vectors mean ,pass:

[0089]

[0090] Obtain the reference value of the remote sensing image to be corrected. ;

[0091] pass:

[0092]

[0093] Obtain the column number of the reference column of the remote sensing image to be corrected. , the column number The corresponding column vector is used as the reference column, and the mean of the reference column is used as the original low-frequency noise intensity. ;

[0094] Step S3: Calibrate the low-frequency noise intensity based on the reference column. ;

[0095] Step S4: Using the reference column as a benchmark, obtain the mean adjustment value of the remote sensing image to be corrected. ;

[0096] Step S5: Utilize the low-frequency noise intensity obtained in step S3. and the mean adjustment value obtained in step S4 Perform pixel correction within column vectors, by

[0097]

[0098] Obtain the pixel values ​​in the corrected remote sensing image. Complete the correction of the remote sensing image to be corrected.

[0099] The correction method described in this embodiment is an efficient and accurate method for correcting low-frequency noise in remote sensing images. Taking the column-direction low-frequency noise of remote sensing images as an example, the low-frequency noise in the remote sensing image to be corrected appears as a gradual color difference from left to right, and the corresponding column-direction statistical features of the image will show a gradual fluctuation trend.

[0100] This implementation extracts the mean of the image column vectors as a feature to measure low-frequency noise. Using the column mean or row mean as the basic processing unit, it does not lose image resolution and has low computational complexity.

[0101] This embodiment selects a noise correction reference column. Since images containing low-frequency noise exhibit a gradual fluctuation in column mean, this embodiment selects a specific image column as a noise-free reference column. Noise reduction is achieved by adjusting the mean values ​​of the remaining columns based on this reference column. The reference column selection process is as follows: First, the average value of the image column mean is calculated as a baseline value, considered the ideal mean value of the image under noise-free conditions. Then, the column whose mean value is closest to the baseline value is selected as the reference column, and its mean value is the ideal column mean value of the image.

[0102] This implementation method accurately measures the low-frequency noise intensity based on the obtained reference column mean, and adaptively achieves radiometric correction of remote sensing images under complex noise characteristics.

[0103] To avoid loss of detail in the corrected image, this implementation method corrects low-frequency noise in the image using mean compensation. Furthermore, the column mean compensation process is expanded to uniformly correct all pixels within a column vector with the same intensity, resulting in an image corrected for low-frequency noise.

[0104] This implementation method, through the above-mentioned selection of noise correction reference columns, refined noise intensity calibration, and low-frequency noise correction, significantly saves processing time and resource consumption, adaptively realizes radiometric correction of remote sensing images under complex noise characteristics, has good applicability and robustness, and can handle low-frequency noise from different satellite data.

[0105] This embodiment provides an example to verify the effectiveness of the proposed low-frequency noise correction method for remote sensing images. This embodiment uses the Jilin-1 Gaofen-03D series satellite to verify the effectiveness of the proposed method. The Jilin-1 Gaofen-03D series satellite is a mass-produced product of Changguang Satellite Technology Co., Ltd. It employs innovative technologies such as lightweight structural design, highly integrated electronic control system, and ultra-high-speed laser data transmission, featuring low cost, low power consumption, low weight, and high resolution. Its satellite imagery includes multispectral images (MSS) composed of four spectral bands: blue, green, red, and near-infrared, and panchromatic images (PAN). Figure 2 Multispectral images of Jilin-1 Gaofen-03D satellite before and after correction in mountainous scenes; Figure 3 These are panchromatic images of the Jilin-1 Gaofen-03D satellite before and after correction in a desert setting.

[0106] like Figure 2 and Figure 3 As shown, Figure 2This is a comparison image of example multispectral remote sensing images described in this embodiment, namely... Figure 2 middle, Figure 2 (a) is a multispectral remote sensing image before low-frequency stripe noise correction. It shows a significant low-frequency chromatic aberration that gradually changes from left to right and runs from top to bottom across the entire image. Visually, the left side of the image is noticeably reddish, and the red chromatic aberration gradually weakens as the area transitions from left to right. The right side, on the other hand, shows a significant green chromatic aberration. The overall color distribution is uneven, which seriously affects the true color of the mountain scene. Figure 2 (b) is a multispectral remote sensing image corrected using the low-frequency stripe noise method described in this embodiment. This image is compared with... Figure 2 (a) In comparison, multispectral images eliminate low-frequency stripe noise. Visually, the color transition from left to right is natural and smooth, presenting a uniform and consistent inherent color of the mountains, accurately restoring the true color characteristics of the mountain scene.

[0107] Figure 3 This is a comparison image of an example panchromatic remote sensing image as described in this embodiment, namely... Figure 3 middle, Figure 3 (a) is a panchromatic remote sensing image before low-frequency stripe noise correction. Before correction, the panchromatic image of the desert scene shows significant low-frequency color difference from left to right, and the color difference runs through the entire image column from top to bottom. Visually, there is a dark low-frequency color difference in the middle area of ​​the image, while the right side of the image turns into a bright color difference. As the area transitions to the right, the bright color difference gradually weakens, and the rightmost side shows a significant dark color difference. The overall distribution of light and dark is uneven, which seriously affects the imaging effect of uniform features such as deserts. Figure 3 (b) is a panchromatic remote sensing image corrected using the low-frequency stripe noise method described in this embodiment. This image is compared with... Figure 3 (a) In comparison, the low-frequency stripe noise of the panchromatic image is eliminated. Visually, the transition between light and dark colors in the image from left to right is natural and smooth, without color shift or abrupt changes in brightness. The overall image presents a uniform and consistent level of brightness, accurately restoring the real imaging characteristics of the desert scene.

[0108] Implementation Method Two: This implementation method further defines the low-frequency noise correction method for remote sensing images described in Implementation Method One. In this implementation method, step S3 specifically includes:

[0109] Step S31, based on the reference column number and the corresponding original low-frequency noise intensity ,pass

[0110]

[0111] Obtain the column number of the reference column of the remote sensing image to be corrected. excluding the first The raw low-frequency noise intensity of the column vector of the column ;

[0112] Step S32: Use the SG filter method to adjust the original low-frequency noise intensity. Filtering is performed to obtain the low-frequency noise intensity. .

[0113] In this embodiment, step S32 specifically involves,

[0114] Step S321, through

[0115]

[0116] Get the length of the sliding window ,in It is a constant;

[0117] Step S322: For the mean of the remote sensing image column to be corrected within the sliding window, perform polynomial fitting using the least squares method, where the polynomial degree is n, to obtain the smoothing coefficient. ;

[0118] Step S323, through

[0119]

[0120] Obtain low frequency noise intensity ,in, The mean value of the remote sensing image column to be corrected within the sliding window;

[0121] Step S324: Repeat steps S321 to S323 by moving the sliding window until the sliding window has moved through all columns of the remote sensing image to be corrected, and finally obtain the low-frequency noise intensity. .

[0122] This embodiment further defines step S3 and provides an example of the scheme in step S3. This method accurately determines the low-frequency noise intensity based on the obtained reference column mean. By calculating the original low-frequency noise intensity and then using the SG filter method to filter the original low-frequency noise intensity, the influence of individual outliers is eliminated, improving the quality of the corrected remote sensing image, avoiding overcorrection during radiometric correction, and adaptively achieving radiometric correction of remote sensing images under complex noise characteristics.

[0123] Implementation Method 3: This implementation method further defines the low-frequency noise correction method for remote sensing images described in Implementation Method 1. In this implementation method, step S4 specifically involves the following steps:

[0124] Step S41, based on the first step described in step S1 Column vector mean and the low-frequency noise intensity described in step S3 ,pass

[0125]

[0126] Obtain the ideal column vector mean ;

[0127] Step S42, through

[0128]

[0129] Obtain the column mean of the remote sensing image image to the left of the reference column. ;

[0130] Step S43, through

[0131]

[0132] Obtain the column mean of the remote sensing image image to the right of the reference column. ;

[0133] Step S44, through

[0134]

[0135] Obtain the mean of the left and right images ;

[0136] Step S45, through

[0137]

[0138] Obtain the mean-adjusted value .

[0139] In this embodiment, the remote sensing image on the left side of the reference column The first column to the second column of the remote sensing image to be corrected Remote sensing imagery of the column. Remote sensing imagery on the right side of the reference column. The first remote sensing image to be corrected Remote sensing images listed in the last column.

[0140] This embodiment further defines step S4 and provides an example of the scheme for step S4. The method uses the mean of the low-frequency noise intensity values ​​of each column as the basis for calculating the mean of the column vector. Compensation is performed to obtain the ideal column vector mean of the corrected image. By calculating the mean of the left and right images It achieves one-time color processing of the images on both sides of the reference column after correction, maintaining color consistency; by obtaining the mean adjustment value of the remote sensing image to be corrected, it realizes mean compensation of the images on both sides of the reference column column by column.

[0141] Implementation Method 4: This implementation method further defines the low-frequency noise correction method for remote sensing images described in Implementation Methods 1 to 3. In this implementation method, row vectors are used instead of column vectors, and the other steps are the same as in Implementation Methods 1 to 3.

Claims

1. A method for low frequency noise correction of remote sensing imagery, characterized in that, The correction method is: Step S1, acquiring a remote sensing image to be corrected, by: a column vector of the first column of the remote sensing image to be corrected a column vector of the first column of the remote sensing image to be corrected a column vector of the first column of the remote sensing image to be corrected wherein, , a column vector of the first column of the remote sensing image to be corrected a column vector of the first column of the remote sensing image to be corrected a column vector of the first column of the remote sensing image to be corrected a column vector of the first column of the remote sensing image to be corrected a column vector of the first column of the remote sensing image to be corrected Step S2, based on the mean of the column vectors of the column of the remote sensing image to be corrected by:​​ obtaining reference values of the remote sensing image to be corrected ; Through: obtaining a column number of a reference column of the remote sensing image to be corrected , the column number , the corresponding column vector as a reference column, and a mean value of the reference column as an original low-frequency noise intensity ; Step S3, calibrating low frequency noise intensity based on the reference column ; Step S4, obtaining a mean adjustment value of the remote sensing image to be corrected based on the reference column ; Step S5, adjusting the low-frequency noise intensity obtained in step S3 by the mean adjustment value obtained in step S4 performing column vector-in pixel correction by​ Obtaining corrected pixel values in a remote sensing image to be corrected ; the correction of the remote sensing image to be corrected is completed.

2. The method of claim 1, wherein, Step S3 is specifically: Step S31, based on the reference column number , and the corresponding original low-frequency noise intensity , by obtaining a column number of a reference column of the remote sensing image to be corrected column other than the original low-frequency noise intensity of the column vector of the column other than the ; Step S32, filtering the original low-frequency noise intensity by using the SG filter method to obtain a low-frequency noise intensity .

3. The method of claim 2, wherein, Step S32 is specifically, Step S321, by obtaining a length of a sliding window wherein is a positive integer; Step S322, using least square method to perform polynomial fitting on the column mean of the remote sensing image to be corrected in the sliding window, the polynomial degree is n, and a smoothing coefficient is obtained ; Step S323, by Obtaining low frequency noise intensity wherein, is the mean of the column of remote sensing images to be corrected within the sliding window. Step S324, repeat the above steps S321 to S323 by moving the sliding window until the sliding window has moved through all columns of the remote sensing image to be corrected, and finally obtain the low-frequency noise intensity .

4. The method of claim 1, wherein, Step S4 is specifically: Step S41, based on the first Column vector Mean of And the low-frequency noise intensity described in step S3 , by Obtaining ideal column vector mean ; Step S42, by obtaining a column mean value of the reference column left remote sensing image ; Step S43, by obtaining column mean values of the reference column right side remote sensing image ; Step S44, by Obtaining left and right side image mean values ; Step S45, by obtaining a mean adjustment value .

5. The method of claim 1, wherein, Replace the column vector with the row vector.

6. A system for low frequency noise correction of remote sensing imagery, characterized in that, Comprise the following modules: Module one, for acquiring a remote sensing image to be corrected, by: a column vector of the first column of the remote sensing image to be corrected a mean value of the column vector wherein, , a column number of the remote sensing image to be corrected, a row number of the remote sensing image to be corrected, a pixel value of a pixel in the first row and the first column of the remote sensing image to be corrected a pixel value of a pixel in the first row and the first column of the remote sensing image to be corrected a pixel value of a pixel in the first row and the first column of the remote sensing image to be corrected​ Module two, for calculating a first column vector of the column vectors of the mean of the column vectors of the mean of the column vectors obtaining reference values of the remote sensing image to be corrected ; Through: obtaining a column number of a reference column of the remote sensing image to be corrected , the column number a corresponding column vector as the reference column, and a mean value of the reference column as an original low-frequency noise intensity ; Module three, for calibrating low frequency noise intensity based on the reference column ; Module four, for obtaining a mean adjustment value of the remote sensing image to be corrected based on the reference column ; Module five, for performing pixel correction within the column vector using the low frequency noise intensity obtained from module three and the mean adjustment value obtained from module four by Obtaining corrected pixel values in a remote sensing image to be corrected ; the correction of the remote sensing image to be corrected is completed.

7. An electronic device, comprising: Include processor, communication interface, memory and communication bus, wherein, processor, communication interface, memory complete mutual communication through communication bus; Memory, for storing computer programs; Processor, for executing the program stored on the memory, to realize the remote sensing image low frequency noise correction method of any one of claims 1-4, 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the remote sensing image low frequency noise correction method of any one of claims 1-4, 5.

Images