Share Email Print
cover

Journal of Applied Remote Sensing

Feature-enhanced spectral similarity measure for the analysis of hyperspectral imagery
Author(s): Qingbo Li; Chunyang Niu
Format Member Price Non-Member Price
PDF $20.00 $25.00

Paper Abstract

In hyperspectral remote sensing, the surface compositional material can be identified by means of spectral matching algorithms. In many cases, the importance of each spectral band to measure spectral similarity is different, whereas the traditional spectral matching algorithms implicitly assume all wavelength-dependent absorption features are equal. This may yield an unsatisfactory performance for spectral matching. To remedy this deficiency, we propose methods called feature-enhanced spectral similarity measures. They are hybrids of the spectral matching algorithms combined with a feature-enhanced space projection, termed feature-enhanced spectral angle measure, feature-enhanced Euclidean distance measure, feature-enhanced spectral correlation measure, and feature-enhanced spectral information divergence. The proposed methods creatively project the original spectra into spectral feature-enhanced space, in which important features for measuring the spectral similarity will be increased to a high degree, whereas features of low importance will be suppressed. In order to demonstrate the effectiveness of the proposed approaches, performances are compared on real hyperspectral image data from Airborne Visible Infrared Imaging Spectrometer. The proposed methods are found to possess significant improvements over the original four spectral matching algorithms.

Paper Details

Date Published: 21 September 2015
PDF: 12 pages
J. Appl. Remote Sens. 9(1) 096008 doi: 10.1117/1.JRS.9.096008
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Qingbo Li, BeiHang Univ. (China)
Chunyang Niu, BeiHang Univ. (China)


© SPIE. Terms of Use
Back to Top