Remote sensing image restoration method combining wave-band clustering with sparse representation

Abstract

The invention provides a remote sensing image restoration method combining wave-band clustering with sparse representation. In order to improve the spatial resolution of a hyperspectral remote sensing image, according to the characteristics of rich spectral dimension information of the hyperspectral image and different noise strength of different wave bands, a multiband image restoration model is built, the wave bands are mutually restrained and complemented by the aid of high similarity of the wave bands and redundant information, and finally, the high-quality hyperspectral image is obtained. Firstly, the wave bands of the hyperspectral image are clustered, and a large number of wave bands are divided into a small number of categories with large relevant information difference. Secondly, a cluster of wave bands in the same category is built into an integral variation training multiband dictionary by the compressive sensing theory, and the dictionary is used for completing image restoration. Relevancy of a plurality of wave bands is sufficiently used for restoring target images, the spectral characteristics of the target images are kept, and restoration results have higher spatial information and spectral information keeping performance.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and relates to a remote sensing image restoration method based on band clustering and sparse expression. Constraints and complementary relationships between the various bands in the image, build a multi-channel image restoration model to obtain high-quality images. Background technique [0002] With the development of information processing technology, people's requirements for images are getting higher and higher. However, due to the influence of satellites, sensors, or the atmosphere, there may be some noise or optical blurring in the finally obtained images. Images are often of low resolution, degraded, deformed and polluted by noise, resulting in the inability to simultaneously obtain high spatial resolution and hyperspectral resolution of remote sensing images, making it difficult to meet people's visual needs. When it is difficult or expensive to obtain high-quality...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art, to provide a remote sensing image restoration method that combines band clustering and sparse expression, on the basis of utilizing the high correlation between bands for restoration, and adding robustness to noise Sparse prior, training the dictionary and performing image restoration on the same type of band with high correlation at the same time, the dictionary constructed in this way can avoid the situation of insufficient information when training the dictionary for the band, and can add information from other bands to construct a multi-channel Sparse models, exploiting multi-band direct correlations, correct poor restorations caused by improperly trained dictionaries in single-band restoration

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