Remote Sensing Applications and Decision Support

Feature-enhanced spectral similarity measure for the analysis of hyperspectral imagery

[+] Author Affiliations
Qingbo Li, Chunyang Niu

Beihang University, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, School of Instrumentation Science and Opto-Electronics Engineering, Xueyuan Road No. 37, Haidian District, Beijing 100191, China

J. Appl. Remote Sens. 9(1), 096008 (Sep 21, 2015). doi:10.1117/1.JRS.9.096008
History: Received June 15, 2015; Accepted August 11, 2015
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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.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Qingbo Li and Chunyang Niu
"Feature-enhanced spectral similarity measure for the analysis of hyperspectral imagery", J. Appl. Remote Sens. 9(1), 096008 (Sep 21, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.096008


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