NDLI: A novel adaptive classification method for hyperspectral data using manifold regularization kernel machines

Content Provider	IEEE Xplore Digital Library
Author	Wonkook Kim Crawford, M.
Copyright Year	2009
Description	Author affiliation: Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette, IN, USA (Wonkook Kim; Crawford, M.)
Abstract	Remote sensing data sets are often difficult to compare directly due to environmental changes between acquisitions of two data sets. This paper proposes an adaptive framework for robust classification when no reference data are available in a new area or time period. Labels of test data are recovered during iterative applications of kernel machines by reflecting geometry of unlabeled samples via the manifold regularization term, so that the labeled/unlabeled samples form clusters on the data manifold. A one-against-one scheme is used for the extension of the binary classifier to multiclass problems, where semi-labels are used for iterative training of classifier. The proposed method is applied to a series of data pair of Hyperion and AVIRIS hyperspectral data and compared to other non-adaptive classification methods.
Starting Page	1
Ending Page	4
File Size	405599
Page Count	4
File Format	PDF
ISBN	9781424446865
DOI	10.1109/WHISPERS.2009.5289052
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-26
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Population drift Hyperspectral sensors Manifold regularization Remote sensing Manifolds Support vector machines Geometry Adaptive classifier Hyperspectral data Classification Semisupervised learning Robustness Kernel Kernel machines Hyperspectral imaging Testing
Content Type	Text
Resource Type	Article

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