A remote sensing image enhancement method

Abstract

The present invention discloses a remote sensing image enhancement method, comprising the following steps: A. synchronously capturing a high-resolution image and a low-resolution image of a remote sensing object; B. comparing the high-resolution image and the low-resolution image to screen for characteristic regions; C. extracting characteristic pixel sets from the characteristic regions marked in the low-resolution image; D. deleting the characteristic pixel sets extracted in step C from the high-resolution image, and then extracting the remaining characteristic pixel sets; E. performing enhancement processing on the characteristic pixel sets extracted in steps C and D, then replacing the corresponding pixel sets in the high-resolution image with the enhanced characteristic pixel sets, and then performing an antilogarithmic transformation on the high-resolution image. The present invention can overcome the shortcomings of the prior art, simplify the enhancement processing steps, and improve image processing efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, in particular to a remote sensing image enhancement method. Background Art

[0002] Remote sensing monitoring is a common method for conducting large-scale near-surface monitoring. After acquiring raw remote sensing images, they need to be enhanced to improve the accuracy of subsequent remote sensing image analysis. Existing remote sensing image enhancement methods are complex, computationally intensive, and inefficient. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a remote sensing image enhancement method, which can solve the deficiencies of the existing technology, simplify the enhancement processing steps, and improve the image processing efficiency.

[0004] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0005] A remote sensing image enhancement method comprises the following steps:

[0006] A. Synchronously capture high-resolution and low-resolution images of remote sensing objects;

[0007] B. Compare the high-resolution image with the low-resolution image and screen the feature areas;

[0008] C. Extracting a set of feature pixels from the marked feature area in the low-resolution image;

[0009] D. Delete the feature pixel set extracted in step C from the high-resolution image, and then extract the remaining feature pixel sets;

[0010] E. Enhance the feature pixel set extracted in step C and step D, then use the enhanced feature pixel set to replace the corresponding pixel set in the high-resolution image, and then perform an antilogarithmic transform on the high-resolution image.

[0011] Preferably, in step B, screening the characteristic region includes the following steps:

[0012] B1. Perform logarithmic transformation on the high-resolution image and the low-resolution image respectively;

[0013] B2. Perform high-pass filtering on the transformed high-resolution image and the low-resolution image to obtain a high-frequency image, and perform low-pass filtering on the transformed high-resolution image and the low-resolution image to obtain a low-frequency image;

[0014] B3. Traverse the high-resolution high-frequency image, low-frequency image, and low-resolution high-frequency image, low-frequency image respectively, mark the areas in each image where the grayscale variation range is less than the set threshold, then compare the marked areas in the four images, delete the areas marked in all four images in the high-resolution image and the low-resolution image, and the remaining areas are the feature areas.

[0015] Preferably, in step C, extracting a feature pixel set from a feature region marked in the low-resolution image comprises the following steps:

[0016] C1, taking any grayscale extreme point in the feature area as the center point, gradually expand the recognition radius outward;

[0017] C2. After each expansion of the recognition radius, if there is no feature pixel within the recognition radius, then calculate the angle between the Euclidean direction of the newly added pixel point and the center point and the grayscale gradient direction of the newly added pixel point, and the deviation between the grayscale gradient amplitude of the newly added pixel point and the grayscale difference between the newly added pixel point and the center origin. If the calculated direction angle and grayscale deviation are both less than the set threshold, then the pixel point is determined to be a feature pixel. If there is a feature pixel within the recognition radius, then calculate the angle between the Euclidean direction of the newly added pixel point and the feature pixel with the closest Euclidean distance within the recognition radius and the grayscale gradient direction of the newly added pixel point, and the deviation between the grayscale gradient amplitude of the newly added pixel point and the grayscale difference between the newly added pixel point and the feature pixel with the closest Euclidean distance. If the calculated direction angle and grayscale deviation are both less than the set threshold, then the pixel point is determined to be a feature pixel.

[0018] Preferably, in step D, extracting a feature pixel set from a marked feature region in a high-resolution image comprises the following steps:

[0019] D1. Randomly select several pixels in the feature area as starting points;

[0020] D2: Starting from the starting point, mark the pixels adjacent to the starting point along the grayscale gradient direction of the starting point, and then mark the pixels adjacent to the most recently marked pixel along the grayscale gradient direction until all the pixels to be marked are marked. The marking process ends and the remaining pixels in the closed area surrounded by the marked pixels are deleted;

[0021] D3. Repeat step D2 until all remaining pixels in the feature area are marked, and the remaining pixels are feature pixels.

[0022] Preferably, in step E, a gamma correction method is used to enhance the feature pixel set.

[0023] The beneficial effect brought about by adopting the above-mentioned technical solution is that: the present invention abandons the technical route of directly enhancing remote sensing images in the prior art, and instead uses high-resolution and low-resolution images to first compare and extract feature pixel sets, and then only enhances the extracted feature pixel sets, while ensuring the image processing effect, effectively reducing the overall amount of calculation. By utilizing the sensitivity of high- and low-frequency images to grayscale changes, the feature areas are first marked, and then the range of the extracted feature pixel sets is narrowed. Since the low-resolution image has a small volume and low image accuracy, a direct calculation method is adopted to extract the feature pixel sets; since the high-resolution image has a large volume and high image accuracy, an iterative elimination method is adopted to extract the feature pixel sets. Thus, the purpose of improving image processing efficiency is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a specific embodiment of the present invention. DETAILED DESCRIPTION

[0025] Reference Figure 1 , a specific embodiment of the present invention includes the following steps:

[0026] A. Synchronously capture high-resolution and low-resolution images of remote sensing objects;

[0027] B. Compare the high-resolution image with the low-resolution image and screen the feature areas;

[0028] Screening feature areas includes the following steps:

[0029] B1. Perform logarithmic transformation on the high-resolution image and the low-resolution image respectively;

[0030] B2. Perform high-pass filtering on the transformed high-resolution image and the low-resolution image to obtain a high-frequency image, and perform low-pass filtering on the transformed high-resolution image and the low-resolution image to obtain a low-frequency image;

[0031] B3. Traverse the high-resolution high-frequency image, low-frequency image, and low-resolution high-frequency image, low-frequency image, respectively, and mark the areas in each image where the grayscale variation range is less than the set threshold. Then compare the marked areas in the four images, and delete the marked areas in the high-resolution image and the low-resolution image. The remaining areas are the feature areas.

[0032] C. Extracting a feature pixel set from a feature region marked in a low-resolution image; comprising the following steps:

[0033] C1, taking any grayscale extreme point in the feature area as the center point, gradually expand the recognition radius outward;

[0034] C2. After each expansion of the recognition radius, if there is no feature pixel within the recognition radius, then calculate the angle between the Euclidean direction of the newly added pixel point and the center point and the grayscale gradient direction of the newly added pixel point, and the deviation between the grayscale gradient amplitude of the newly added pixel point and the grayscale difference between the newly added pixel point and the center origin. If the calculated direction angle and grayscale deviation are both less than the set threshold, then the pixel point is determined to be a feature pixel. If there is a feature pixel within the recognition radius, then calculate the angle between the Euclidean direction of the newly added pixel point and the feature pixel with the closest Euclidean distance within the recognition radius and the grayscale gradient direction of the newly added pixel point, and the deviation between the grayscale gradient amplitude of the newly added pixel point and the grayscale difference between the newly added pixel point and the feature pixel with the closest Euclidean distance. If the calculated direction angle and grayscale deviation are both less than the set threshold, then the pixel point is determined to be a feature pixel.

[0035] D. Delete the feature pixel set extracted in step C from the high-resolution image, and then extract the remaining feature pixel sets; the following steps are included:

[0036] D1. Randomly select several pixels in the feature area as starting points;

[0037] D2: Starting from the starting point, mark the pixels adjacent to the starting point along the grayscale gradient direction of the starting point, and then mark the pixels adjacent to the most recently marked pixel along the grayscale gradient direction until all the pixels to be marked are marked. The marking process ends and the remaining pixels in the closed area surrounded by the marked pixels are deleted;

[0038] D3, repeat step D2 until all the remaining pixels in the feature area are marked, and the remaining pixels are the feature pixels;

[0039] E. Enhance the feature pixel sets extracted in steps C and D using a gamma correction method, then use the enhanced feature pixel sets to replace the corresponding pixel sets in the high-resolution image, and then perform an antilogarithmic transform on the high-resolution image.

[0040] In the description of the present invention, it should be understood that the terms "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0041] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A remote sensing image enhancement method, characterized in that The following steps are involved: A. Synchronously capture high-resolution and low-resolution images of remote sensing objects; B. Compare the high-resolution image with the low-resolution image and screen the feature areas; C. Extracting a set of feature pixels from the marked feature area in the low-resolution image; C1, taking any grayscale extreme point in the feature area as the center point, gradually expand the recognition radius outward; C2. After each expansion of the recognition radius, if there is no feature pixel within the recognition radius, then calculate the angle between the Euclidean direction of the newly added pixel point and the center point and the grayscale gradient direction of the newly added pixel point, and the deviation between the grayscale gradient amplitude of the newly added pixel point and the grayscale difference between the newly added pixel point and the center origin. If the calculated direction angle and grayscale deviation are both less than the set threshold, then the pixel point is determined to be a feature pixel. If there is a feature pixel within the recognition radius, then calculate the angle between the Euclidean direction of the newly added pixel point and the feature pixel with the closest Euclidean distance within the recognition radius and the grayscale gradient direction of the newly added pixel point, and the deviation between the grayscale gradient amplitude of the newly added pixel point and the grayscale difference between the newly added pixel point and the feature pixel with the closest Euclidean distance. If the calculated direction angle and grayscale deviation are both less than the set threshold, then the pixel point is determined to be a feature pixel. D. Delete the feature pixel set extracted in step C from the high-resolution image, and then extract the remaining feature pixel sets; D1. Randomly select several pixels in the feature area as starting points; D2: Starting from the starting point, mark the pixels adjacent to the starting point along the grayscale gradient direction of the starting point, and then mark the pixels adjacent to the most recently marked pixel along the grayscale gradient direction until all the pixels to be marked are marked. The marking process ends and the remaining pixels in the closed area surrounded by the marked pixels are deleted; D3, repeat step D2 until all the remaining pixels in the feature area are marked, and the remaining pixels are the feature pixels; E. Enhance the feature pixel set extracted in step C and step D, then use the enhanced feature pixel set to replace the corresponding pixel set in the high-resolution image, and then perform an antilogarithmic transform on the high-resolution image.

2. The remote sensing image enhancement method according to claim 1, wherein: In step B, screening the feature area includes the following steps: B1. Perform logarithmic transformation on the high-resolution image and the low-resolution image respectively; B2. Perform high-pass filtering on the transformed high-resolution image and the low-resolution image to obtain a high-frequency image, and perform low-pass filtering on the transformed high-resolution image and the low-resolution image to obtain a low-frequency image; B3. Traverse the high-resolution high-frequency image, low-frequency image, and low-resolution high-frequency image, low-frequency image respectively, mark the areas in each image where the grayscale variation range is less than the set threshold, then compare the marked areas in the four images, delete the areas marked in all four images in the high-resolution image and the low-resolution image, and the remaining areas are the feature areas.

3. The remote sensing image enhancement method according to claim 1, wherein: In step E, a gamma correction method is used to enhance the feature pixel set.

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