NDLI: Boundary based discriminant analysis for feature extraction in classification of hyperspectral images

Content Provider	IEEE Xplore Digital Library
Author	Imani, M. Ghassemian, H.
Copyright Year	2014
Description	Author affiliation: Fac. of Electr. & Comput. Eng., Tarbiat Modares Univ., Tehran, Iran (Imani, M.; Ghassemian, H.)
Abstract	For classification of hyperspectral images, particularly using limited training samples, supervised feature extraction is an approach for reduction of dimensionality, overcoming the Hughes phenomenon and increasing the classification accuracies. Classic and popular feature extraction methods such as linear discriminant analysis (LDA) have not good efficiency in small sample size situation because of the singularity problem. Another supervised method, nonparametric weighted feature extraction (NWFE) is efficient for solving some problems of LDA and works well using limited training samples. We propose an efficient approach for improving of discriminant analysis (DA) method. The proposed method, named boundary based discriminant analysis (BBDA), uses only the boundary training samples for DA to increases the classification accuracy. Moreover, with using a regularization technique, it overcomes the singularity problem in DA. The experimental results obtained on two popular real hyperspectral data sets (one agriculture image and one urban image) show the improvement of BBDA respect to some conventional supervised feature extraction methods in small sample size situation.
Starting Page	424
Ending Page	429
File Size	295063
Page Count	6
File Format	PDF
e-ISBN	9781479953592
DOI	10.1109/ISTEL.2014.7000741
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-09
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Accuracy Limited training samples Classification Boundary training samples Feature extraction Educational institutions Reliability Hyperspectral imaging
Content Type	Text
Resource Type	Article

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