In this letter, we explore the use of self-dual attribute profiles (SDAPs) for the classification of hyperspectral images. The 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.
Extended Self-Dual Attribute Profiles for the Classification of Hyperspectral Images / Cavallaro, Gabriele; Dalla Mura, Mauro; Benediktsson, Jon Atli; Bruzzone, Lorenzo. - In: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. - ISSN 1545-598X. - STAMPA. - 12:8(2015), pp. 1690-1694. [10.1109/LGRS.2015.2419629]
Extended Self-Dual Attribute Profiles for the Classification of Hyperspectral Images
Dalla Mura, Mauro;Benediktsson, Jon Atli;Bruzzone, Lorenzo
2015-01-01
Abstract
In this letter, we explore the use of self-dual attribute profiles (SDAPs) for the classification of hyperspectral images. The 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.File | Dimensione | Formato | |
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