NDLI: Nonparametric feature selection and support vector machine for polarimetric SAR data classification

Content Provider	IEEE Xplore Digital Library
Author	Maghsoudi, Y. Collins, M. Leckie, D.G.
Copyright Year	2011
Description	Author affiliation: University of Calgary, Deparment of Geomatics, AB, Canada (Maghsoudi, Y.; Collins, M.) \|\| Natural Resources Canada, Canadian Forest Service, Victoria, BC, Canada, V8Z 1M5 (Leckie, D.G.)
Abstract	There are a wide range of SAR parameters that may be extracted from polarimetric SAR data. In very complex scenes like forests it is very useful to exploit the discriminative power offered by these features. Most of these features are of complex and sometimes unknown statistical properties. For this, the conventional feature selection algorithms cannot be applied. To account for this, a nonparametric separability measure was used as the evaluation function in the feature selection process. The measure is defined as the determinant of the between-class scatter matrix to the determinant of the sum of within-class scatter matrices. To improve the classification accuracy, the method was also used in the context of a class-based feature selection. The approach, first selects a feature subset for each class, train a SVM classifier on each selected feature subset and finally combines the outputs of the classifiers in a combination scheme. The Wishart classification algorithm is used as the reference method. Experimental results using Radarsat-2 data indicate that using the developed method improves the classification accuracy.
Starting Page	2857
Ending Page	2860
File Size	280784
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049810
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Covariance matrix Accuracy Radar imaging Feature extraction Matrix decomposition Support vector machines Scattering class-based PolSAR data forest classification non-parametric feature selection
Content Type	Text
Resource Type	Article

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