Hyperspectral image denoising method based on non-local low-rank tensor decomposition of subspace

Abstract

The invention discloses a hyperspectral image denoising method based on non-local low-rank tensor decomposition of a subspace, a high-dimensional hyperspectral image has a low-rank characteristic in the subspace, and for a subspace feature map of the hyperspectral image, combined low-rank constraint is carried out on the subspace feature map by using spatial non-local self-similarity and tensor Tucker decomposition. And the denoising capability of the mixed noise is further improved. The technical scheme is mainly used for solving the problem of noise interference in the hyperspectral image, and Gaussian noise and sparse noise contained in the hyperspectral image can be removed. A high-dimensional hyperspectral image has a low-rank characteristic in a subspace, and for a subspace feature map of the hyperspectral image, combined low-rank constraint is performed on the subspace feature map by using spatial non-local self-similarity and tensor Tucker decomposition, so the denoising capability of mixed noise is further improved.

Description

technical field [0001] The invention relates to a hyperspectral image denoising method based on subspace non-local low-rank tensor decomposition, which can be used in the technical field of image processing. Background technique [0002] With the development of remote sensing sensor technology, relying on airborne and spaceborne hyperspectral imaging technology provides end users with rich spectral, spatial and temporal information. The number of bands in hyperspectral images has grown from dozens to hundreds, which can more comprehensively describe the spectral characteristics of objects in the observation area. The rich spectral information in hyperspectral images has been significantly developed and widely used in many challenging Earth observation tasks and continues to develop in fine-grained land cover classification, mineral mapping, water quality assessment, precious agriculture, urban planning and monitoring, Disaster management and prediction, hidden target detect...

AI Technical Summary

Problems solved by technology

However, the existing sparse low-rank denoising algorithms for hyperspectral images mainly consider the local and space-spectrum similarities of hyperspectral images, and the low-dimensional subspace characteristics of hyperspectral images need to be further studied.

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