Remote sensing image fusion method based on multi-dimensional morphologic element analysis

Abstract

The invention discloses a remote sensing image fusion method based on multi-dimensional morphologic element analysis, and belongs to the field of crossing signal processing and remote sensing image processing. The method is that the morphologic element analysis is respectively carried out for a high-resolution remote sensing image and a multi-spectrum remote sensing image under different dimension; the iterative shrinkage method is carried out to perform sparse decomposition; a target image to be fused is divided into texture components and cartoon components based on a plurality of dimensions; the cartoon component and the noise component in the high-solution image, and the texture component and the noise components in the multi-spectrum image are removed; the effective dimension texture component in the high-solution image and the cartoon component in the multi-spectrum image are remained and subjected to spare reconstruction, so as to obtain the fusion image. With the adoption of the method, the high-resolution remote sensing image and the multi-spectrum remote sensing image are effectively fused; the space resolution is improved and the spectrum distortion is reduced by being compared with the existing fusion method; in addition, the rate is greatly increased by being compared with the existing sparse reconstruction method.

Description

Technical field: [0001] The invention belongs to the intersection field of signal analysis and remote sensing image processing, and is a remote sensing image fusion method based on multi-scale morphological component analysis. Background technique: [0002] With the rapid development of computer technology, aerospace technology, and remote sensing technology, image resource acquisition methods (multi-sensor, multi-platform) are increasingly abundant, and the obtained remote sensing images also present the characteristics of multi-spatial resolution, multi-spectrum, and multi-temporal equality. At present, the problems of remote sensing images are as follows: ① A single remote sensing image often only contains part of the ground features, but cannot reflect all the content; ② Compared with single-source data, multi-source data are complementary, and there is a certain amount of Redundancy; ③ Due to the large amount of information and high redundancy of multi-source remote sen...

AI Technical Summary

Problems solved by technology

At present, the problems of remote sensing images are as follows: ① A single remote sensing image often only contains part of the ground features, but cannot reflect all the content; ② Compared with single-source data, multi-source data are complementary, and there is a certain amount of Redundancy; ③ Due to the large amount of information and high redundancy of multi-source remote sensing data, its utilization rate is low

[0004] 1) The fusion results of IHS transformation method, Brovey method and principal component analysis method have large spectral distortion, and the spatial resolution has a large room for improvement; (refer to comparison file 1, comparison file 2, and comparison file 3)

[0005] 2) The wavelet transform analysis method will introduce more artificial noise, and there is a large spectral distortion; (refer to comparative document 4)

[0006] 3) The sparse reconstruction method based on base tracking and matching tracking has a huge amount of calculation, and small images are often used in simulation experiments for testing, and it is difficult to put real large remote sensing images into practical application

However, the existing morphological component analysis method is carried out at a single scale, which is unfavorable for the analysis and decomposition of remote sensing images with complex surface features

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