Angle-based band selection for material identification in hyperspectral processing

Nirmal Keshava

doi:10.1117/12.487534

23 September 2003 Angle-based band selection for material identification in hyperspectral processing

Nirmal Keshava

Proceedings Volume 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX; (2003) https://doi.org/10.1117/12.487534
Event: AeroSense 2003, 2003, Orlando, Florida, United States

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.

Citation Download Citation

Nirmal Keshava "Angle-based band selection for material identification in hyperspectral processing", Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); https://doi.org/10.1117/12.487534

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available