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Journal of Applied Remote Sensing

Remote sensing image fusion method based on multiscale morphological component analysis
Author(s): Jindong Xu; Mengying Ni; Yanjie Zhang; Xiangrong Tong; Qiang Zheng; Jinglei Liu
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Paper Abstract

A remote sensing image (RSI) fusion method based on multiscale morphological component analysis (m-MCA) is presented. Our contribution describes a new multiscale sparse image decomposition algorithm called m-MCA, which we apply to RSI fusion. Building on MCA, m-MCA combines curvelet transform bases and local discrete cosine transform bases to build a multiscale decomposition dictionary, and controls the entries of the dictionary to decompose the image into texture components and cartoon components with different scales. The effective scale texture component of high-resolution RSI and the cartoon component of multispectral RSI are selected to reconstruct the fusion image. Compared with state-of-the-art fusion methods, the proposed fusion method obtains higher spatial resolution and lower spectral distortion with reduced computation load in numerical experiments.

Paper Details

Date Published: 9 June 2016
PDF: 14 pages
J. Appl. Remote Sens. 10(2) 025018 doi: 10.1117/1.JRS.10.025018
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Jindong Xu, Yantai Univ. (China)
Mengying Ni, Yantai Univ. (China)
Yanjie Zhang, Yantai Univ. (China)
Xiangrong Tong, Yantai Univ. (China)
Qiang Zheng, Yantai Univ. (China)
Jinglei Liu, Yantai Univ. (China)


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