Remote sensing image fusion method based on sparse representation

Abstract

The invention discloses a remote sensing image fusion method based on sparse representation. The method comprises the following steps of: firstly, establishing a linear regression model between a multispectral image and a brightness component thereof; secondly, performing sparse representation on a panchromatic image and the multispectral image by using high and low resolution dictionaries respectively, and acquiring sparse representation coefficients of the brightness component of the multispectral image according to the linear regression model; thirdly, extracting detail components according to the sparse representation coefficients of the panchromatic image and the brightness component, and implanting the detail components to the sparse representation coefficients of each band of the multispectral image under a general component replacement fusion framework; and finally, performing image restoration to obtain a multispectral image with high spatial resolution. According to the method, the sparse representation technology is introduced into the field of remote sensing image fusion, so that the defect that high spatial resolution and spectral information cannot be simultaneously preserved in the prior art is overcome; and the fusion result of the method is superior to that of the conventional remote sensing image fusion method on the aspects of spectral preservation and spatial resolution improvement.

Description

technical field [0001] The invention relates to image processing technology, in particular to a remote sensing image fusion method based on sparse representation. Background technique [0002] With the continuous development of remote sensing technology, remote sensing image data obtained by various satellite sensors with different spatial resolution, temporal resolution, and spectral resolution provide abundant and valuable resources for human earth observation. However, for the remote sensing image of the optical system, the spatial resolution and the spectral resolution are contradictory. Under a certain signal-to-noise ratio, the improvement of the spectral resolution is at the expense of the spatial resolution. Panchromatic images have high spatial resolution and rich spatial detail information, and can express the detailed features of ground objects in detail, but have less spectral information; multispectral images have rich spectral information, which is beneficial t...

AI Technical Summary

Problems solved by technology

Fusion based on color space component replacement is generally fused in the pixel gray space of the image, such as IHS (Intensity-Hue-Saturation), PCA (Principal Component Analysis), etc. This type of algorithm effectively improves the spatial resolution of the fused image rate but introduces severe spectral distortion at the same time

Based on the ARSIS model fusion, the spatial resolution is improved by inferring the missing high-frequency components of MS images, such as HPF (High-pass Filtering), WTF (Wavelet Transform Fusion), etc. These algorithms solve the serious spectral distortion of the component replacement fusion algorithm problem, but the fused multispectral image is prone to over-injection or cancellation of details

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