Color Difference Optimization Method for Multi-source Remote Sensing Image Processing

Abstract

Abstract This paper proposes an approach to solve the problem of color inconsistency after the fusion and splicing of multi-spectral images of multi-source domestic satellites, by using regularly division the high-resolution remote sensing images, block by block computing the multiple linear regression and percent clip stretch. First of all, the remote sensing image is divided into blocks and multiple linear regression is carried out separately in each block. Secondly, on the basis of the image to be corrected after regression, percentage truncation and stretching are carried out on the over-concentrated gray band, so that the gray histogram distribution of the gray band is similar to that of the reference image. This scheme ensures that all pixels participate in the calculation, which not only improves the accuracy of the model, but also avoids the problems of long processing time and memory overflow caused by the large amount of high-resolution remote sensing image data. At the same time, each scene image selects an appropriate truncation percentage according to the evaluation index to solve the problem of excessive color difference caused by too concentrated image band gray. The experimental results show that the scheme in this paper can effectively solve the problems of too large color difference and too concentrated gray scale in image band. Compared with traditional methods, it has better performance in model accuracy, color retention and time efficiency.