Pansharpening With Joint Local Low Rank Decomposition and Hierarchical Geometric Filtering

IEEE Access

Min Wang¹  Yuteng Gao²  Chen Yang³  Shuyuan Yang⁴  Chengtian Song⁴ 

[1] School of Artificial Intelligence, Xidian University, Xi&x2019;School of Electronics and Information, Northwestern Polytechnical University, Xi&x2019;School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China;an, China;

关键词: Pansharpening;    joint local low-rank decomposition;    hierarchical geometric filtering;    spectral correlation coefficient;   

DOI  :  10.1109/ACCESS.2019.2940482

来源: DOAJ

【 摘 要 】

Extracting matched details of the PANchromatic (PAN) image and injecting them into the MultiSpectral (MS) images, is very crucial in pansharpening. In this paper, a new pansharpening method based on Joint Local Low Rank Decomposition (JLLRD) and Hierarchical Geometric Filtering (HGF) is proposed. First, a cascaded geometric filtering is performed on the PAN and MS images, to extract their multi-scale directional details. Then a joint local low rank decomposition is developed to deduce low-rank and sparse components for injection. Finally, an adaptive injection rule based on spectral correlation coefficient, is designed to further reduce spectral distortion of the fused images. Several experiments are taken to investigate the performance of the proposed JLLRD-HGF method, and the results show that it can extract more accurate injection details and produce less spectral and spatial distortions than its counterparts.

【 授权许可】

Unknown