Spectral derivative feature coding for hyperspectral signature analysis

Authors:
Chein-I Chang;Sumit Chakravarty;Hsian-Min Chen;Yen-Chieh Ouyang
Affiliations:
Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA and Department o ...;Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA;Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan, ROC and Department of Medical Research, China Medical University Hospital, Taichung, Taiwan, ROC and Depart ...;Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan, ROC
Venue:
Pattern Recognition
Year:
2009

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Abstract

This paper presents a new approach to hyperspectral signature analysis, called spectral derivative feature coding (SDFC). It is derived from texture features used in texture classification to dictate gradient changes among adjacent bands in characterizing spectral variations so as to improve better spectral discrimination and classification. In order to evaluate its performance, two known binary coding methods, spectral analysis manager (SPAM) and spectral feature-based binary coding (SFBC) are used to conduct comparative analysis. Experimental results demonstrate that the proposed SDFC performs more effectively in capturing spectral characteristics than do SPAM and SFBC.