NDLI: A New Least Squares Subspace Projection Approach to Unmix Hyperspectral Data

Content Provider	IEEE Xplore Digital Library
Author	Liaoying Zhao Kai Zhang
Copyright Year	2009
Abstract	Linearly constrained discriminant analysis (LCDA) and orthogonal subspace projection (OSP) are both explored in hyperspectral image classification and have shown promise in signature detection, discrimination and classification. However, the two subspace projection approaches cannot directly estimate the signature abundance. The OSP has been extended by a least squares orthogonal subspace projection (LSOSP) to estimate the signature abundance while LCDA has not. The solution of LCDA turns out to be a constrained version of OSP implemented with a data whitening process and the means of samples as signatures. Due to this fact, following the same idea for extending OSP to LSOSP, in this paper, a modified linearly constrained discriminant analysis (MLCDA) is proposed for unmixing hyperspectral data, which can directly estimate the signature abundance. Experiment results obtained from both artificial simulated and practical remote sensing data demonstrate that the MLCDA algorithm performs better than least squares method and the LSOSP.
Starting Page	350
Ending Page	354
File Size	431897
Page Count	5
File Format	PDF
ISBN	9780769535715
DOI	10.1109/GCIS.2009.228
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral sensors Image analysis Subspace constraints Least squares approximation Intelligent systems Least squares methods Remote sensing Pixel Hyperspectral imaging Image classification
Content Type	Text
Resource Type	Article

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