Panchromatic image sharpening method based on low-rank decomposition of directional multi-scale group

Abstract

The invention discloses a panchromatic image sharpening method based on directional multi-scale group low-rank decomposition. The steps of its implementation are: (1) input source image; (2) obtain LMS and dPan images; (3) calculate multispectral image MS and downsampled panchromatic image dPan spectral correlation coefficient; (4) non-subsampled contour wavelet decomposition; (5) construction of data matrix; (6) matrix low-rank decomposition; (7) reconstruction of high and low frequency sparse matrix; (8) Inject high and low frequency sparse matrix (9) non-subsampled contour wavelet inverse transform; (10) output high-resolution image. This invention uses non-subsampling contour wavelet changes and matrix low-rank decomposition to extract the contour structure information and details of the panchromatic image, and adopts a new high and low frequency injection model to reduce the spectral distortion caused by over-injection of the panchromatic image, and finally obtains a better It is good to preserve spectral information and high-resolution images with more obvious edge details.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to remote sensing applications, a panchromatic image sharpening method based on low-rank decomposition of multi-scale groups in directions in the technical field of remote sensing image processing. The invention can be applied to the remote sensing fields such as target recognition of roads, airports, buildings, etc., and forest resources investigation. The panchromatic image sharpening is performed through the multispectral image with low resolution and high spectral rate and the panchromatic image with high resolution and low spectral rate to obtain a multispectral image with high resolution and high spectral rate. The invention can improve classification accuracy when used in remote sensing application field, and can provide clear and high-quality images in target recognition field, especially for target positioning and recognition effect of roads, buildings and the ...

AI Technical Summary

Problems solved by technology

Although this method can achieve better results, it still has the disadvantage that this method injects too much panchromatic image information, which will cause certain color distortion.

This method can obtain a better high-resolution multispectral image, but the method still has the disadvantage that the final high-resolution multispectral image loses some information of the multispectral image, which will cause certain spectral distortion. distortion

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