Edge abnormal pixel clipping processing method and system based on multi-band remote sensing image

Abstract

The application discloses a kind of based on multi-band remote sensing image edge abnormal pixel clipping processing method and system. First, remote sensing image file is acquired, and the raster data corresponding to remote sensing image file is converted into three-dimensional array and is stored;Each band image in three-dimensional array is summed and is binarized to obtain binary image matrix C;Then, according to the all 1 matrix constructed in advance, the edge of binary image matrix C is handled, and the binary image matrix F of removing edge abnormal part is obtained;Finally, the binary image matrix F of removing edge abnormal part is carried out conditional and operation assignment with image matrix, and the target image after edge abnormal pixel clipping is obtained.The application solves the problem that the effective range of image is not accurate due to the abnormal value caused by edge jitter in the shooting process of satellite sensor.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, in particular to an edge abnormal pixel clipping processing method and system based on multi-band remote sensing images. BACKGROUND

[0002] Multi-band remote sensing images refer to images with multiple wavelength bands (usually more than 3) obtained through remote sensing technology. These wavelength bands are different in the electromagnetic spectrum and can include visible light, infrared, ultraviolet, radar, etc. Each band corresponds to different energy and reflectivity, thus presenting different ground feature characteristics.

[0003] In the data processing process, all current processing processes are aimed at processing internal abnormal data of images. Common processing methods include image smoothing, image enhancement, and neighborhood filling. However, in the existing processing process, abnormal values caused by edge jitter during shooting can lead to inaccurate effective range of images. SUMMARY

[0004] Therefore, the embodiments of the present application provide an edge abnormal pixel clipping processing method and system based on multi-band remote sensing images, which can solve the problems existing in the current processing process.

[0005] In a first aspect, an edge abnormal pixel clipping processing method based on multi-band remote sensing images is provided, which includes:

[0006] Obtaining a remote sensing image file and converting raster data corresponding to the remote sensing image file into a three-dimensional array for storage; wherein the three-dimensional array contains row, column, and band information of the remote sensing image;

[0007] Summing each band image in the three-dimensional array to obtain an image matrix, and performing binaryzation processing on the image matrix to obtain a binaryzation image matrix C; wherein the pixel values of all bands at each position in the matrix are added to obtain a total pixel value at each position;

[0008] Performing edge processing on the binaryzation image matrix C according to a pre-constructed all-1 matrix to obtain a binaryzation image matrix F with the edge abnormal part removed;

[0009] Performing conditional AND operation assignment on the binaryzation image matrix F with the edge abnormal part removed and the image matrix to obtain a target image after edge abnormal pixel clipping.

[0010] Optionally, the obtaining of the remote sensing image file and the conversion of the raster data corresponding to the remote sensing image file into a three-dimensional array for storage includes:

[0011] Setting the background value of the remote sensing image file to 0 and the effective value range to 1-255.

[0012] Optionally, the edge processing of the binary image matrix C is performed according to a pre-constructed all-1 matrix, and the method comprises the following steps:

[0013] An all-1 matrix E of 2*2 is constructed;

[0014] The all-1 matrix E is translated on the edge of the binary image matrix C in the order of row i, column j, and band k;

[0015] The edge of the binary image matrix C is revalued based on the all-1 matrix E and the numerical value of the corresponding element position of the binary image matrix C.

[0016] Optionally, the revaluing of the edge of the binary image matrix C based on the all-1 matrix E and the numerical value of the corresponding element position of the binary image matrix C comprises the following steps:

[0017] The all-1 matrix E is sequentially moved on the binary image matrix C, and when the area covered by the all-1 matrix E on the binary image matrix C is [1, 1; 1, 1], the corresponding position of the output image of the binary image matrix C is valued as 1, and otherwise, the value is 0.

[0018] Optionally, the binary image matrix F after removing the edge abnormal part is subjected to conditional AND operation and valuation with the image matrix to obtain the target image after the edge abnormal pixel is cropped, and the method comprises the following steps:

[0019] The binary image matrix F and the image matrix are subjected to conditional AND operation, and if the same position is true, the original value of the image matrix is retained, otherwise, the value is 0;

[0020] All bands are sequentially calculated to obtain the target image after the edge abnormal pixel is cropped.

[0021] In a second aspect, a system for edge abnormal pixel cropping processing based on multi-band remote sensing images is provided, and the system comprises:

[0022] An acquisition module is configured to acquire a remote sensing image file and convert raster data corresponding to the remote sensing image file into a three-dimensional array for storage; wherein the three-dimensional array contains row, column, and band information of the remote sensing image;

[0023] A binary module is configured to sum each band image in the three-dimensional array to obtain an image matrix, and perform binary processing on the image matrix to obtain a binary image matrix C; wherein each position in the matrix is added to obtain a total pixel value at each position;

[0024] An edge processing module is configured to perform edge processing on the binary image matrix C according to a pre-constructed all-1 matrix to obtain a binary image matrix F after removing the edge abnormal part.

[0025] An assignment module is configured to perform a conditional and operation assignment between the binary image matrix F after removing the edge abnormal part and the image matrix to obtain the target image after cutting the edge abnormal pixels.

[0026] Optionally, the acquisition module comprises:

[0027] The background value of the remote sensing image file is set to 0, and the effective value range is 1-255.

[0028] Optionally, the edge processing module comprises:

[0029] A 2*2 all-1 matrix E is constructed;

[0030] The all-1 matrix E is translated on the edge of the binary image matrix C in the order of row i, column j and band k;

[0031] The edge of the binary image matrix C is re-assigned based on the numerical value of the corresponding element position of the all-1 matrix E and the binary image matrix C.

[0032] Optionally, the re-assignment of the edge of the binary image matrix C based on the numerical value of the corresponding element position of the all-1 matrix E and the binary image matrix C comprises:

[0033] When the area covered by the all-1 matrix E on the binary image matrix C is [1, 1; 1, 1], the corresponding position of the output image of the binary image matrix C is assigned as 1, and otherwise, it is assigned as 0.

[0034] Optionally, the assignment module comprises:

[0035] If the same position of the binary image matrix F and the image matrix is true, the original value of the image matrix is retained, otherwise, it is assigned as 0;

[0036] All bands are calculated in sequence to obtain the target image after cutting the edge abnormal pixels.

[0037] In the technical scheme provided by the embodiments of the application, a remote sensing image file is first acquired, and the raster data corresponding to the remote sensing image file is converted into a three-dimensional array for storage; the sum of each band image in the three-dimensional array is calculated and binarized to obtain a binary image matrix C; the binary image matrix C is edge-processed according to a pre-constructed all-1 matrix to obtain a binary image matrix F after removing the edge abnormal part; and a conditional and operation assignment is performed between the binary image matrix F after removing the edge abnormal part and the image matrix to obtain a target image after cutting the edge abnormal pixels. It can be seen that the application provides a basis for accurately determining the range of orthographic results in the conventional image production process, and solves the problem of inaccurate image effective range caused by abnormal values due to edge jitter of a satellite sensor during shooting. Attached Figure Description

[0038] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0039] Figure 1 A flowchart of edge anomalous pixel cropping processing based on multi-band remote sensing imagery is provided for embodiments of this application;

[0040] Figure 2 This is a schematic diagram illustrating edge processing in an embodiment of this application;

[0041] Figure 3 This is a schematic diagram illustrating the conditional AND operation assignment in the embodiments of this application. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] In the description of this invention, the terms “comprising,” “having,” and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are expressly listed, but may also include other steps or units that are not expressly listed but are inherent to these processes, methods, products, or apparatuses, or steps or units added based on further optimizations of the inventive concept.

[0044] This invention aims to provide a foundation for accurately determining the range of orthorectified images during conventional image production, and to address the problem of inaccurate effective image range caused by anomalies resulting from edge jitter in satellite sensors during image capture. For details, please refer to... Figure 1 The document illustrates a flowchart of a method for cropping edge-abnormal pixels based on multi-band remote sensing images, provided in an embodiment of this application. This method may include the following steps:

[0045] Step 101: Obtain the remote sensing image file and convert the raster data corresponding to the remote sensing image file into a three-dimensional array for storage.

[0046] Where, the three-dimensional array contains the row, column and band information of the remote sensing image. Remote sensing image files are generally in raster data format, common formats include TIFF, JPEG, PNG, etc. Among them, the TIFF format is most commonly used for the storage and transmission of remote sensing images, because it supports higher compression ratio and color depth, and can handle large-scale data.

[0047] In this embodiment, first set the remote sensing image background value to 0, and the effective value range to 1-255.

[0048] The remote sensing image file is stored in a 3-dimensional matrix form. Storing the remote sensing image file in a 3-dimensional matrix form means converting the raster data into a three-dimensional array for storage. This three-dimensional array contains the row, column and band information of the image. By storing the remote sensing image file in a 3-dimensional matrix form, it is more convenient to process and analyze the image data, such as pixel-based classification, target recognition, etc.

[0049] For example, the stored 3-dimensional matrix can be , where the image has R rows, C columns, and N bands, is the 2-dimensional matrix form of the kth band of the image, where a is the pixel value of each band at position ijk, i is the row, j is the column, and k is the band number.

[0050] Step 102, summing each band image in the three-dimensional array to obtain an image matrix, and performing binaryzation processing on the image matrix to obtain a binaryzation image matrix C.

[0051] Where, the pixel value of each position in the matrix at each position of all bands is added to obtain a total pixel value at each position.

[0052] In this step, the summing of each band image is specifically .

[0053] Binaryzation processing is performed on matrix B to obtain a binaryzation image matrix C, , element b ij is greater than 0, replaced by 1, otherwise replaced by 0. Matrix C is a two-dimensional matrix containing only element values of 1 and 0, where the region with element value of 1 is the effective region of the remote sensing image, and 0 is the background region.

[0054] Step 103, according to the all-1 matrix constructed in advance, edge processing is performed on the binaryzation image matrix C to obtain a binaryzation image matrix F that removes the edge abnormal part.

[0055] A 2*2 matrix is constructed, with the origin defined as the position of element .

[0056] The all-one matrix E is shifted along the edge of the binarized image matrix C in the order of row i, column j, and band k. The edges of the binarized image matrix C are then revalued based on the values ​​of the corresponding elements in the all-one matrix E and the binarized image matrix C.

[0057] Specifically, E is translated in the order of first i, then j, and finally k. When the origin of E is translated to pixel (x,y) of image C, if E is completely contained in the overlapping area of ​​image C at (x,y) (that is, the image value of C corresponding to the element position of 1 in E is also 1), then the corresponding pixel (x,y) of the output image is assigned a value of 1; otherwise, it is assigned a value of 0.

[0058] When E moves sequentially on C, the position of the corresponding output image will be 1 when the area of ​​C it covers is [1,1;1,1] (that is, '1' in E is a subset of the covered area).

[0059] like Figure 2 The diagram illustrates edge processing in an embodiment of this application. The left-hand matrix C of a 2x2 matrix of all ones is processed to obtain the right-hand matrix F.

[0060] This step yields a binarized image matrix F, which is an image matrix reduced by one edge pixel. If n pixels need to be reduced, repeat this step to obtain a binarized image of the same size as the original image with the edge anomalies removed.

[0061] Step 104: Perform a conditional AND operation between the binarized image matrix F (with the edge abnormalities removed) and the image matrix to obtain the target image after cropping the edge abnormality pixels.

[0062] Will Perform a conditional AND operation with the binarized image matrix F. If the conditional AND operation is true at the same position, then retain the result. Otherwise, assign the original value; calculate the background value of 0. Calculate N bands sequentially to obtain the results. .

[0063] Figure 3 This is a schematic diagram illustrating the conditional AND operation assignment in an embodiment of this application. The diagram shows the target image on the right obtained by performing a conditional AND operation between the binarized image matrix F on the left and the image matrix in the middle.

[0064] This application also provides a system for cropping edge-abnormal pixels based on multi-band remote sensing images. The system includes:

[0065] The acquisition module is used to acquire remote sensing image files and convert the raster data corresponding to the remote sensing image files into a three-dimensional array for storage; wherein, the three-dimensional array contains the row, column and band information of the remote sensing image;

[0066] The binarization module is configured to sum up each band image in the three-dimensional array to obtain an image matrix, and perform binarization processing on the image matrix to obtain a binarization image matrix C; wherein, the pixel values of all bands at each position in the matrix are added to obtain a total pixel value at each position.

[0067] The edge processing module is configured to perform edge processing on the binarization image matrix C according to a pre-constructed all-1 matrix to obtain a binarization image matrix F from which edge abnormal parts are removed.

[0068] The assignment module is configured to perform conditional AND operation assignment on the binarization image matrix F from which edge abnormal parts are removed and the image matrix to obtain a target image after edge abnormal pixels are cropped.

[0069] In an optional embodiment of the present application, the acquisition module comprises:

[0070] The background value of the remote sensing image file is set to 0, and the effective value range is 1-255.

[0071] In an optional embodiment of the present application, the edge processing module comprises:

[0072] An all-1 matrix E of 2*2 is constructed.

[0073] The all-1 matrix E is translated on the edge of the binarization image matrix C in the order of row i first, column j second, and band k last.

[0074] The edge of the binarization image matrix C is re-assigned based on the numerical values of the corresponding element positions of the all-1 matrix E and the binarization image matrix C.

[0075] In an optional embodiment of the present application, the re-assignment of the edge of the binarization image matrix C based on the numerical values of the corresponding element positions of the all-1 matrix E and the binarization image matrix C comprises:

[0076] The all-1 matrix E moves on the binarization image matrix C in sequence, and when the area covered by the all-1 matrix E on the binarization image matrix C is [1, 1; 1, 1], the corresponding position of the output image of the binarization image matrix C is assigned a value of 1, and otherwise, a value of 0 is assigned.

[0077] In an optional embodiment of the present application, the assignment module comprises:

[0078] If the binarization image matrix F and the image matrix are true at the same position, the original value of the image matrix is retained, otherwise, a value of 0 is assigned.

[0079] All bands are sequentially calculated to obtain a target image after edge abnormal pixels are cropped.

[0080] The edge abnormal pixel clipping processing system based on multi-band remote sensing images provided by the embodiments of the present application is used to implement the edge abnormal pixel clipping processing method based on multi-band remote sensing images. The specific limitations of the edge abnormal pixel clipping processing system based on multi-band remote sensing images can be referred to the limitations of the edge abnormal pixel clipping processing method based on multi-band remote sensing images, which will not be repeated here. Each part of the edge abnormal pixel clipping processing system based on multi-band remote sensing images can be realized by software, hardware, and combinations thereof, in whole or in part. The above modules can be embedded in or independent of the processor in the device in hardware form, or can be stored in the memory in the device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0081] In addition, in one embodiment of the present application, a computer readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the edge abnormal pixel clipping processing method are implemented.

[0082] The computer readable storage medium provided by the embodiments has similar implementation principles and technical effects to the above method embodiments, which will not be repeated here.

[0083] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In the embodiments provided by the present application, any reference to memory, storage, database, or other medium can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in M forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (SyMchliMk) DRAM (SLDRAM), memory bus (RaMbus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0084] Any combination of the technical features in the above-described embodiments can be made, and for the sake of brevity, not all possible combinations are described, however, it is to be understood that the application embraces all such possible combinations.

[0085] The above-described embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the application. It should be pointed out that for ordinary skilled persons in the art, some modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the patent protection scope of the present application should be subject to the appended claims.

Claims

1. A multi-band remote sensing image-based edge abnormal pixel clipping processing method, characterized in that, The method comprises: acquire remote sensing image file, and convert the raster data corresponding to the remote sensing image file into a three-dimensional array for storage; wherein the three-dimensional array contains the row, column and band information of the remote sensing image; sum each band image in the three-dimensional array to obtain an image matrix, and perform binaryzation processing on the image matrix to obtain a binaryzation image matrix C; wherein the pixel values of all bands at each position in the matrix are added to obtain a total pixel value at each position; perform edge processing on the binaryzation image matrix C according to a pre-constructed all-1 matrix to obtain a binaryzation image matrix F that removes the edge abnormal part; perform conditional and operation assignment on the binaryzation image matrix F that removes the edge abnormal part and the image matrix to obtain a target image after edge abnormal pixel trimming.

2. The edge-exceptional-pixel cropping process method of claim 1, wherein, The acquiring remote sensing image file and converting the raster data corresponding to the remote sensing image file into a three-dimensional array for storage comprises: set the background value of the remote sensing image file to 0, and the effective value range to 1-255.

3. The edge-exceptional-pixel cropping process method of claim 1, wherein, The edge processing on the binaryzation image matrix C according to a pre-constructed all-1 matrix comprises: construct an all-1 matrix E of 2*2; translate the all-1 matrix E at the edge of the binaryzation image matrix C in the order of row i first, column j second, and band k last; reassign the value of the binaryzation image matrix C edge based on the corresponding element positions of the all-1 matrix E and the binaryzation image matrix C.

4. The edge-exceptional-pixel crop processing method of claim 3, wherein, The reassigning the value of the binaryzation image matrix C edge based on the corresponding element positions of the all-1 matrix E and the binaryzation image matrix C comprises: When the all-1 matrix E moves on the binaryzation image matrix C and the area covered by the all-1 matrix E on the binaryzation image matrix C is [1, 1; 1, 1], the corresponding position of the output image of the binaryzation image matrix C is assigned a value of 1, and the rest is assigned a value of 0.

5. The edge-exceptional-pixel cropping process method of claim 1, wherein, The conditional and operation assignment on the binaryzation image matrix F that removes the edge abnormal part and the image matrix to obtain a target image after edge abnormal pixel trimming comprises: If the binaryzation image matrix F and the image matrix are the same at the same position, the original value of the image matrix is retained, otherwise it is assigned a value of 0; all bands are calculated in turn to obtain a target image after edge abnormal pixel trimming.

6. A multi-band remote sensing image-based edge abnormal pixel clipping processing system, characterized in that, The system comprises: an acquisition module configured to acquire a remote sensing image file and convert raster data corresponding to the remote sensing image file into a three-dimensional array for storage; wherein the three-dimensional array contains row, column and band information of the remote sensing image; a binaryzation module configured to sum each band image in the three-dimensional array to obtain an image matrix, and perform binaryzation processing on the image matrix to obtain a binaryzation image matrix C; wherein the pixel values of all bands at each position in the matrix are added to obtain a total pixel value at each position; an edge processing module configured to perform edge processing on the binaryzation image matrix C according to a pre-constructed all-1 matrix to obtain a binaryzation image matrix F that removes the edge abnormal part; an assignment module configured to perform conditional and operation assignment on the binaryzation image matrix F that removes the edge abnormal part and the image matrix to obtain a target image after edge abnormal pixel trimming.

7. The edge-exceptional-pixel crop processing system of claim 6, wherein, The acquisition module comprises: Set the background value of the remote sensing image file to 0, and the effective value range is 1~255.

8. The edge-exceptional-pixel crop processing system of claim 6, wherein, The edge processing module comprises: A 2*2 all-1 matrix E is constructed; The all-1 matrix E is translated on the edge of the binary image matrix C in the order of row i, column j and band k; The edge of the binary image matrix C is revalued based on the numerical value of the corresponding element position of the all-1 matrix E and the binary image matrix C.

9. The edge-exceptional-pixel crop processing system of claim 8, wherein, The revaluing of the edge of the binary image matrix C based on the numerical value of the corresponding element position of the all-1 matrix E and the binary image matrix C comprises: When the all-1 matrix E moves on the binary image matrix C and covers the area [1,1;1,1] of the binary image matrix C, the corresponding position of the output image of the binary image matrix C is valued as 1, and the remaining cases are valued as 0.

10. The edge-exceptional-pixel crop processing system of claim 6, wherein, The valuation module comprises: If the same position of the binary image matrix F and the image matrix I is true, the original value of the image matrix is retained, otherwise it is valued as 0; All bands are calculated in turn to obtain the target image after the edge abnormal pixel is cropped.

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