9 June 2016 Remote sensing image fusion method based on multiscale morphological component analysis
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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.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Jindong Xu, Jindong Xu, Mengying Ni, Mengying Ni, Yanjie Zhang, Yanjie Zhang, Xiangrong Tong, Xiangrong Tong, Qiang Zheng, Qiang Zheng, Jinglei Liu, 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 . Submission:
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