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Proceedings Paper

Angle-based band selection for material identification in hyperspectral processing
Author(s): Nirmal Keshava
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Paper Abstract

In this article we present a method for hyperspectral band selection that yields superior classification results while only using a subset of the available bands. The approach originates from a comprehensive physical and mathematical understanding of the distance metrics used to compare hyperspectral signals, and it exploits an exact decomposition of a common metric, the Spectral Angle Mapper (SAM), to select bands which increase the angular contrast between target classes. Using real spectroradiometer and sensor data collected by the HYDICE sensor, the technique significantly improves the discrimination performance for two spectrally similar classes, while using only a fraction of the available bands. The approach is extended to a hierarchical architecture for material identification using spectral libraries that is shown to outperform the traditional angle-based classifier which employs all available bands. Consequently, better material identification performance can be achieved using significantly fewer bands, thus introducing dramatic benefits for the design and utilization of spectral libraries.

Paper Details

Date Published: 23 September 2003
PDF: 12 pages
Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); doi: 10.1117/12.487534
Show Author Affiliations
Nirmal Keshava, MIT Lincoln Lab. (United States)


Published in SPIE Proceedings Vol. 5093:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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