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Proceedings Paper

Hyperspectral pixel classification from coded-aperture compressive imaging
Author(s): Ana Ramirez; Gonzalo R. Arce; Brian M. Sadler
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Paper Abstract

This paper describes a new approach and its associated theoretical performance guarantees for supervised hyperspectral image classification from compressive measurements obtained by a Coded Aperture Snapshot Spectral Imaging System (CASSI). In one snapshot, the two-dimensional focal plane array (FPA) in the CASSI system captures the coded and spectrally dispersed source field of a three-dimensional data cube. Multiple snapshots are used to construct a set of compressive spectral measurements. The proposed approach is based on the concept that each pixel in the hyper-spectral image lies in a low-dimensional subspace obtained from the training samples, and thus it can be represented as a sparse linear combination of vectors in the given subspace. The sparse vector representing the test pixel is then recovered from the set of compressive spectral measurements and it is used to determine the class label of the test pixel. The theoretical performance bounds of the classifier exploit the distance preservation condition satisfied by the multiple shot CASSI system and depend on the number of measurements collected, code aperture pattern, and similarity between spectral signatures in the dictionary. Simulation experiments illustrate the performance of the proposed classification approach.

Paper Details

Date Published: 10 May 2012
PDF: 8 pages
Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 84010J (10 May 2012); doi: 10.1117/12.926417
Show Author Affiliations
Ana Ramirez, Univ. of Delaware (United States)
Gonzalo R. Arce, Univ. of Delaware (United States)
Brian M. Sadler, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 8401:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X
Harold Szu, Editor(s)

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