Imaging spectrum image sparse representation method based on ground object class classification redundant dictionary

Abstract

The invention discloses an imaging spectrum image sparse representation method based on a ground object class classification redundant dictionary. The method comprises K-means, K-SVD-based classification redundant dictionary training and imaging spectrum image full-wave band sparse representation. Through unsupervised K-means clustering, imaging spectrum image information is classified into corresponding subsets according to ground object class; and through classification dictionary sparse decomposition of imaging spectrum images, full-wave band information quality of a reconstructed image is improved under the premise of sparsity. The characteristic that the ground objects, the spectrum images of which are similar, have similar spectrum curves is fully utilized, thereby preventing introducing of wrong information, ensuring the quality of the reconstructed spectrum image and effectively avoiding the problem of spectral distortion caused by independent operation of each wave band.

Description

technical field [0001] The invention relates to a sparse representation method of an imaging spectral image, in particular to a sparse representation method of an imaging spectral image based on a redundant dictionary of classification of ground objects. Background technique [0002] Imaging spectroscopy technology is a new remote sensing technology that developed rapidly in the 1980s. It obtains the radiation information of surface features in dozens or even hundreds of bands at the same time with ultra-high spectral resolution at the nanometer scale, so it can acquire ground features While collecting the spatial information of the target, it detects its internal physical characteristics and composition. Imaging spectroscopy technology is widely used in resource exploration, geological exploration, disaster relief and other fields. [0003] Similar surface areas have similar reflectance values ​​and therefore similar spectral curves. The imaging spectral image can be rega...

AI Technical Summary

Problems solved by technology

The basis tracking algorithm is extremely complex because it needs to minimize a global objective function in all dictionary vectors

[0007] The sparse representation method of the imaging spectral image in the existing technology does not fully consider the ability to maintain the spectral dimension information in the imaging spectral image, resulting in a certain spectral distortion in the high-resolution image reconstructed by the sparse representation signal

In addition, there are many bands in hyperspectral images, and it is often unbearable to perform sparse decomposition on all bands.

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