Remote sensing image fusion method based on multiscale morphological component analysis

Jindong Xu; Mengying Ni; Yanjie Zhang; Xiangrong Tong; Qiang Zheng; Jinglei Liu

doi:10.1117/1.JRS.10.025018

9 June 2016 Remote sensing image fusion method based on multiscale morphological component analysis

Jindong Xu, Mengying Ni, Yanjie Zhang, Xiangrong Tong, Qiang Zheng, Jinglei Liu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 2, 025018 (June 2016). https://doi.org/10.1117/1.JRS.10.025018

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.

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Jindong Xu, Mengying Ni, Yanjie Zhang, Xiangrong Tong, Qiang Zheng, and Jinglei Liu "Remote sensing image fusion method based on multiscale morphological component analysis," Journal of Applied Remote Sensing 10(2), 025018 (9 June 2016). https://doi.org/10.1117/1.JRS.10.025018

Published: 9 June 2016

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