Structure sparse representation-based remote sensing image fusion method

Abstract

The invention discloses a structure sparse representation-based remote sensing image fusion method. An adaptive weight coefficient calculation model is used for solving a luminance component of a multi-spectral image, similar image blocks are combined into a structure group, a structure group sparse model is used for solving structure group dictionaries and group sparse coefficients for the luminance component and a panchromatic image, an absolute value maximum rule is applied to partial replacement of the sparse coefficients of the panchromatic image, new sparse coefficients are generated, the group dictionary and the new sparse coefficients of the panchromatic image are used for reconstructing a high-spatial resolution luminance image, and finally, a universal component replacement model is used for fusion to acquire a high-resolution multi-spectral image. The method of the invention introduces the structure group sparse representation in the remote sensing image fusion method, overcomes the limitation that the typical sparse representation fusion method only considers a single image block, and compared with the typical sparse representation method, the method of the invention has excellent spectral preservation and spatial resolution improvement performance, and greatly shortens the dictionary training time during the remote sensing image fusion process.

Description

technical field [0001] The invention relates to image processing technology, in particular to a remote sensing image fusion method based on structural sparse representation. Background technique [0002] In many remote sensing applications, such as land use classification change detection, map updating and disaster early warning monitoring require the use of hyperspectral and high spatial resolution remote sensing images. Due to the limitation of radiation energy, the spatial resolution and spectral resolution of images obtained by remote sensing sensors are contradictory. Multispectral (MS) images are rich in spectral information, but their spatial resolution is low. Panchromatic (PAN) images with high spatial resolution can accurately obtain detailed information of targets, but their spectral information is less. By fusing the spectral and spatial information provided by MS images and PAN images, the fused images not only have high spatial resolution, but also retain the ...

AI Technical Summary

Problems solved by technology

Sparse representations of structural groups are beginning to be used in image super-resolution and image denoising, achieving better performance than classical sparse representations, but these studies are all for single image processing, and the fusion method of multiple images is still blank

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