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

Seizing on sparsity in nonlinear hyperspectral unmixing for enhanced image compression

[+] Author Affiliations
Andrea Marinoni, Paolo Gamba

University of Pavia, Telecommunications and Remote Sensing Laboratory, Department of Electrical, Computer, and Biomedical Engineering, Via Ferrata 5, I-27100 Pavia, Italy

J. Appl. Remote Sens. 10(4), 042007 (Aug 08, 2016). doi:10.1117/1.JRS.10.042007
History: Received February 11, 2016; Accepted July 22, 2016
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Abstract.  Reducing the size of the data on-ground with no information loss represents a strong challenge for the scientific community, since Earth observation (EO) data volumes have strongly and steadily grown during the last 10 years and the need for more efficient compression methods is growing stronger. High-accuracy processing methods employed for EO data understanding and quantifying may result in effective methods for image compression. We propose to use a robust framework of endmember extraction and nonlinear modeling for the on-ground compression of EO data records, where the distribution of the mixture coefficient is exploited to enhance the compression gain while providing high-accuracy reconstruction. Experimental results over real EO datasets show the actual power of the proposed approach.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Andrea Marinoni and Paolo Gamba
"Seizing on sparsity in nonlinear hyperspectral unmixing for enhanced image compression", J. Appl. Remote Sens. 10(4), 042007 (Aug 08, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.042007


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