Special Section on Sparsity-Driven High Dimensional Remote Sensing Image Processing and Analysis

Super-resolution reconstruction of hyperspectral images using empirical mode decomposition and compressed sensing

[+] Author Affiliations
Zhou Ziyong

China University of Petroleum-Beijing, State Key Laboratory of Petroleum Resources and Prospecting, Geosciences College, 18 Fuxue Road, Changping, Beijing 102200, China

J. Appl. Remote Sens. 10(4), 042011 (Nov 22, 2016). doi:10.1117/1.JRS.10.042011
History: Received January 29, 2016; Accepted October 27, 2016
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Abstract.  Hyperspectral remote sensing provides the possibility of direct detection of material information; however, coarse spatial resolution can restrict the scope of its application. The super-resolution (SR) technique can overcome this problem, but the separate application of SR reconstruction to each spectral band is computationally intensive. We proposed an approach that combines empirical mode decomposition (EMD), single-image SR reconstruction using compressed sensing (CS), and principal component analysis (PCA). EMD was used to extract details from within the images, whereas PCA was implemented to reduce the spectral dimensions of the hyperspectral image cube and to retain meaningful spectral information. The CS-based single-image SR reconstruction involved the use of both the K-SVD algorithm for learning and obtaining an over-complete dictionary, and the orthogonal matching pursuit algorithm for the image reconstruction. Experimental results obtained using an EO-1 hyperion image were used to validate the proposed approach.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Zhou Ziyong
"Super-resolution reconstruction of hyperspectral images using empirical mode decomposition and compressed sensing", J. Appl. Remote Sens. 10(4), 042011 (Nov 22, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.042011


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