Method for fusing Shearlet domain multi-spectral and full-color images based on spectral characteristics

Abstract

The invention relates to a method for fusing Shearlet domain multi-spectral and full-color images based on spectral characteristics. The method comprises the steps of (1) performing IHS transformation for a multi-spectral image; (2) performing Shearlet transformation and decomposition for the BIMFs and the full-color image to obtain a low-frequency sub-band and a plurality of high-frequency sub-band; (3) building a fusion rule and selecting fusion coefficients, namely, decomposing the low-frequency coefficients through laplacian pyramid, selecting the low-frequency fusion coefficients by the comparing and bigger selection method, and fusing the corresponding high-frequency coefficients according to the fusion rule based on PCNN (Pulse Coupled Neural Network); (4) performing IHS reverse transformation to obtain the final fused image. With the adoption of the fusion algorithm, the spectrum information of the images is protected while the space details expression capacity is improved, and thus the fusion effect is improved.

Description

technical field [0001] The invention relates to a method in the technical field of image processing, in particular to a method for fusion of multispectral and panchromatic images in Shearlet domain based on spectral characteristics. Background technique [0002] Remote sensing images cannot have high spatial resolution and high spectral resolution at the same time, and spectral resolution needs to be used to improve spatial resolution. Remote sensing image fusion processes redundant multi-source data in space or time according to certain rules, obtains more accurate and richer information than any single data, and generates a synthetic image with new spatial, spectral, and temporal characteristics. [0003] Panchromatic images have high spatial resolution and are good at expressing the detailed features of ground objects, but their spectral resolution is insufficient; multispectral images are rich in spectral information, which is good for identifying the contours of ground ...

AI Technical Summary

Problems solved by technology

[0005] In order to achieve a better fusion effect, more and more researches combine the component substitution method with the multi-resolution analysis method; the fusion method of IHS transform combined with wavelet transform is one of the representatives. Firstly, the brightness of Pan image and MS image Component preprocessing, and then perform wavelet decomposition, replace the high-frequency coefficients of the luminance component with the high-frequency coefficients of the panchromatic image, retain the low-frequency components of the MS image, and obtain a new luminance component through fusion processing. Since the spectral information carried by it is protected, it must be The spectral distortion is reduced to a certain extent; similarly using the fusion method of IHS transform combined with Contourlet transform, the luminance component of the MS image is first matched with the histogram of the PAN image, and the detailed information of the PAN image is injected into the luminance component by using multi-resolution analysis to obtain High-resolution principal component; since the fusion is only performed between the luminance component and the PAN image, the spectral distortion problem of the fused image is partially reduced; although the above method reduces the spectral distortion of the fused image, but because it includes down-sampling and up- The sampling operation produces pseudo-Gibbs phenomenon, which loses part of the spatial information of the Pan image. The spatial resolution of the fused image is often inferior to the traditional IHS fusion method. At the same time, the fusion result is sensitive to the selection of wavelet basis functions and decomposition levels. The parameter selection more complex

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