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Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis

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Dalla Mura, Mauro, Villa, Alberto, Benediktsson, Jon Atli, Chanussot, Jocelyn, Bruzzone, Lorenzo·2011·IEEE Geoscience and Remote Sensing Letters

Abstract

In this letter, a technique based on independent component analysis (ICA) and extended morphological attribute profiles (EAPs) is presented for the classification of hyperspectral images. The ICA maps the data into a subspace in which the components are as independent as possible. APs, which are extracted by using several attributes, are applied to each image associated with an extracted independent component, leading to a set of extended EAPs. Two approaches are presented for including the computed profiles in the analysis. The features extracted by the morphological processing are then classified with an SVM. The experiments carried out on two hyperspectral images proved the effectiveness of the proposed technique.

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Metadata

Authors
Dalla Mura, M., Villa, A., Benediktsson, J. A., Chanussot, J., & Bruzzone, L.
Venue
IEEE Geoscience and Remote Sensing Letters
Published
2011-05
Volume
8
Issue
3
Pages
542-546
ISSN
  • 1545-598X (ISSN-L)
  • 1558-0571
OpenAlex
W2159070926
Cited by
419
References
0
FWCI
27.09
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis. https://app.cochat.ai/papers/10.1109/lgrs.2010.2091253
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Dalla Mura, M., Villa, A., Benediktsson, J. A., Chanussot, J., & Bruzzone, L. (2011). Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/lgrs.2010.2091253

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