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Proceedings Paper

Classification performance of a block-compressive sensing algorithm for hyperspectral data processing
Author(s): Fernando X. Arias; Heidy Sierra; Emmanuel Arzuaga
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Paper Abstract

Compressive Sensing is an area of great recent interest for efficient signal acquisition, manipulation and reconstruction tasks in areas where sensor utilization is a scarce and valuable resource. The current work shows that approaches based on this technology can improve the efficiency of manipulation, analysis and storage processes already established for hyperspectral imagery, with little discernible loss in data performance upon reconstruction. We present the results of a comparative analysis of classification performance between a hyperspectral data cube acquired by traditional means, and one obtained through reconstruction from compressively sampled data points. To obtain a broad measure of the classification performance of compressively sensed cubes, we classify a commonly used scene in hyperspectral image processing algorithm evaluation using a set of five classifiers commonly used in hyperspectral image classification. Global accuracy statistics are presented and discussed, as well as class-specific statistical properties of the evaluated data set.

Paper Details

Date Published: 17 May 2016
PDF: 12 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 984005 (17 May 2016); doi: 10.1117/12.2224542
Show Author Affiliations
Fernando X. Arias, Univ. de Puerto Rico Mayagüez (United States)
Heidy Sierra, Memorial Sloan-Kettering Cancer Ctr. (United States)
Emmanuel Arzuaga, Univ. de Puerto Rico Mayagüez (United States)

Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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