Abstract

In this letter, we explore the use of self-dual attribute profiles (SDAPs) for the classification of hyperspectral images.
\nThe hyperspectral data are reduced into a set of components by nonparametric weighted feature extraction (NWFE), and a morphological processing is then performed by the SDAPs separately on each of the extracted components. Since the spatial information extracted by SDAPs results in a high number of features, the NWFE is applied a second time in order to extract a fixed number of features, which are finally classified. The experiments are carried out on two hyperspectral images, and the support vector machines and random forest are used as classifiers. The effectiveness of SDAPs is assessed by comparing its results against those obtained by an approach based on extended APs.

Keywords

Hyperspectral imagingPattern recognition (psychology)Computer scienceRandom forestArtificial intelligenceSupport vector machineFeature extractionNonparametric statisticsDual (grammatical number)Data setSet (abstract data type)Data miningMathematicsStatistics

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Publication Info

Year
2015
Type
article
Volume
12
Issue
8
Pages
1690-1694
Citations
35
Access
Closed

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Gabriele Cavallaro, Mauro Dalla Mura, Jón Atli Benediktsson et al. (2015). Extended Self-Dual Attribute Profiles for the Classification of Hyperspectral Images. IEEE Geoscience and Remote Sensing Letters , 12 (8) , 1690-1694. https://doi.org/10.1109/lgrs.2015.2419629

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DOI
10.1109/lgrs.2015.2419629