Research Papers

Effects of linear projections on the performance of target detection and classification in hyperspectral imagery

[+] Author Affiliations
Yi Chen, Trac D. Tran

The Johns Hopkins University, Department of Electrical and Computer Engineering, 3400 N. Charles Street, Baltimore, Maryland 21218

Nasser M. Nasrabadi

US Army Research Laboratory, 2800 Powder Mill Road, Adelphi, Maryland 20783

J. Appl. Remote Sens. 5(1), 053563 (November 14, 2011). doi:10.1117/1.3659894
History: Received April 18, 2011; Accepted October 21, 2011; Revised October 19, 2011; Published November 14, 2011; Online November 14, 2011
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We explore the use of several linear dimensionality reduction techniques that can be easily integrated into the hyperspectral imaging sensor. We investigate their effect on the performance of classical target detection and classification techniques for hyperspectral images. Specifically, each N-dimensional spectral pixel is embedded to an M-dimensional measurement space with MN by a linear transformation (e.g., random measurement matrices, uniform downsampling, principal component analysis). The detectors/classifiers are then applied to the M-dimensional measurement vectors and their performances are compared to those obtained from the entire N-dimensional spectrum. Through extensive experiments on several hyperspectral imagery data sets, we demonstrate that only a small amount of measurements are necessary to achieve comparable performance to that obtained by exploiting the full N-dimensional pixels.

© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation

Yi Chen ; Nasser M. Nasrabadi and Trac D. Tran
"Effects of linear projections on the performance of target detection and classification in hyperspectral imagery", J. Appl. Remote Sens. 5(1), 053563 (November 14, 2011). ; http://dx.doi.org/10.1117/1.3659894


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