NDLI: Comparative Analysis of Feature Extraction Algorithms with Different Rules for Hyperspectral Anomaly Detection

Content Provider	IEEE Xplore Digital Library
Author	Zhenlin Liu Yanfeng Gu Ye Zhang
Copyright Year	2010
Abstract	Anomaly detection is one of the most important applications for hyperspectral images. Conventional algorithm such as Reed–Xiaoli (RX) detector fails to be applied to hyperspectral images, which have high spectral dimensionality and complicated correlation between spectral bands. Therefore, effective feature extraction methods and selection rules are necessary. In this paper, comparative analyses of feature extraction methods with different rules are conducted, illustrating their effects on application of anomaly detection in hyperspectral images. The algorithms for feature extraction include principal component analysis (PCA), minimum noise fraction (MNF) transform and their kernel versions. The rules for feature selection are energy and signal-to-noise (SNR). Furthermore, a local singularity (LS) measure is introduced to select the most singular component transformed for anomaly detection, based on local high-order statistics, i.e., skewness and kurtosis. Numerical experiments are performed on real hyperspectral images. The results show that using KPCA with LS measure greatly improves detection performance of the conventional RX algorithm and achieves satisfying effect, and the LS measure is more effective than other rules.
Starting Page	293
Ending Page	296
File Size	538395
Page Count	4
File Format	PDF
ISBN	9781424480432
DOI	10.1109/PCSPA.2010.78
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature selection Hyperspectral images Transforms Feature extraction Kernel Anomaly detection Principal component analysis Hyperspectral imaging Signal to noise ratio
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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