Satellite remote sensing image color equalization method based on block adjustment

Abstract

The invention relates to a satellite remote sensing image color equalization method based on block adjustment. The method comprises steps of S1, extracting an image overlapping region according to thespatial position of an image, dividing the image overlapping region into uniform grids, extracting grid features through employing an HOG feature extraction algorithm, and carrying out the optimization screening of grids with the same name through employing the grid features; S2, taking a polynomial as an adjustment model, introducing a reference image for a coefficient array rank depletion problem, introducing virtual control for a pixel value distribution nonuniformity problem, constraining a difficult region with weak connection by adopting an integral gray average value of an overlappingregion, and carrying out integral solution of an introduced weight on color conversion parameters of each image in a measurement region; and S3, performing color correction on each image by utilizingthe color transformation parameters obtained in the S2 to obtain a color-balanced image sequence. Compared with the prior art, the method is advantaged in that brightness and color difference betweenadjacent images can be effectively processed, so color transition in the whole measurement area is natural, and a better effect is achieved.

Description

technical field [0001] The invention relates to satellite remote sensing image processing technology, in particular to a satellite remote sensing image color equalization method based on block network adjustment. Background technique [0002] Remote sensing imagery is an important data source for obtaining global geographic information. However, due to the influence of various factors such as the imaging time of optical remote sensing images, the lighting conditions during imaging, and the different features and topography of each scene image, the color inconsistency between images will be caused. This phenomenon is a direct factor affecting the production quality of subsequent digital products. Even if the images adopt high-precision geometric positioning and geometric splicing methods, due to the inconsistency of colors between images, there will still be obvious image dividing lines. If not processed, Visually, it is difficult to achieve the effect of "one picture" of th...

AI Technical Summary

Problems solved by technology

However, due to the influence of various factors such as the imaging time of optical remote sensing images, the lighting conditions during imaging, and the different terrain features of each scene image, the color inconsistency between images will be caused.

This phenomenon is a direct factor affecting the production quality of subsequent digital products. Even if the images adopt high-precision geometric positioning and geometric splicing methods, due to the inconsistency of colors between images, there will still be obvious image dividing lines. If not processed, Visually, it is difficult to achieve the effect of "one picture" of the survey area

[0003] At present, there are few studies on the uniform color processing of satellite remote sensing images. Unlike close-range images and aerial images, the overlapping degree of satellite images is much smaller than the former. If the overall gray value of the overlapping area is used as the adjustment variable, then when solving The number of parameters to be calculated is far more than the number of equations, which makes it impossible to solve the result; if a matching method such as sift is used to extract points with the same name for adjustment calculation, the number of pixels in a remote sensing image can reach tens of millions or even hundreds of millions of orders of magnitude. The area will also have millions of pixels, even if only 20% of the points are extracted for adjustment calculation, then the entire survey area will have millions of points with the same name, which will cause a coefficient matrix in the later adjustment calculation If the value is too large, the calculation cost will be too large or even unsolvable, and 20% of the points with the same name in the overlapping area may not be able to reflect the overall gray level difference between the two scenes in some images, resulting in the final image color balance processing. fail

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