Weighted multispectral/hyperspectral remote sensing image color homogenizing method based on Wallis filtering and histogram matching
A technology of hyperspectral remote sensing and histogram matching, which is applied in the field of multi/hyperspectral remote sensing image color homogenization, can solve the problems of large data, deviation, and the overall effect ignores the local characteristics of the image, and achieves enhanced image contrast, noise suppression, and improved image quality. The effect of global matching performance
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specific Embodiment approach 1
[0028] Specific implementation mode 1: This implementation mode is based on the Wallis filter + histogram matching method for color uniformity of multiple / hyperspectral remote sensing images. The specific process is as follows:
[0029] Based on the Wallis filter and the traditional histogram matching algorithm, the invention proceeds from local optimization to overall color uniformity, fully utilizes the information richness of the reference image, and performs color uniformity processing on the image to be uniform color. The purpose of the present invention is to keep the overall tone of the processed remote sensing image consistent, the brightness of the entire image is uniform, and the contrast is suitable, so as to achieve a good effect on enhancing the visual sense of the image, and satisfy researchers and post-processing personnel on ground object problems. Based on the interpretation and analysis of the paper, a method for color uniformity of multiple / hyperspectral remo...
specific Embodiment approach 2
[0039] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is: in the step 2, by counting the information amount and location of the mth group of same-band grayscale images X1, X2, X3, ..., Xi, ..., Xn The richness of objects (types of objects), select a reference image as a reference (the image whose reference value Q is the maximum value); the specific process is:
[0040]Step 21, information entropy indicates the amount of information contained in the mth group of grayscale images X1, X2, X3, ..., Xi, ..., Xn in the same band;
[0041] For the mth group of grayscale images X1, X2, X3, ..., Xi, ..., Xn in the same band, it is considered that each image in the mth group of grayscale images in the same band X1, X2, X3, ..., Xi, ..., Xn The gray values are mutually independent samples, and the proportion of each gray value in a single image (one of the gray images X1, X2, X3, ..., Xi, ..., Xn of the same band in the m group) is p={ p 1 ,p ...
specific Embodiment approach 3
[0051] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that two images are taken in the step three, and one is the grayscale images X1, X2, X3, X1, X2, and X3 of the mth group obtained in step two The reference image Xi of ..., Xi, ..., Xn, and the other is the same-band grayscale image Xj (one of X1, X2, X3, ..., Xi, ..., Xn) that overlaps with the reference image;
[0052] Perform block processing on the overlapping area to obtain sub-blocks, and set the sub-block to take the square of an integer as the best, such as 2*2, 3*3...n*n, so that the area of each sub-block is kept at 50*50~100 *In the range of 100 pixels, too many sub-blocks will lead to obvious partitions, and too few will make the description blurred;
[0053] Do local color uniformity on the overlapping area of the same-band grayscale image that overlaps with the reference image and the reference image (do local color uniformity on the overlapping area...
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