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

Dalla Mura, Mauro; Villa, A.; Benediktsson, J. A.; Chanussot, J.; Bruzzone, L.

doi:10.1109/LGRS.2010.2091253

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.

Scheda prodotto non validato

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte di FBK.

Titolo:	Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis
Autori:	Dalla Mura, Mauro A. Villa J. A. Benediktsson J. Chanussot L. Bruzzone mostra contributor esterni nascondi contributor esterni
Data di pubblicazione:	2011
Rivista:	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.
Handle:	http://hdl.handle.net/11582/51383
Appare nelle tipologie:	1.1 Articolo in rivista

File in questo prodotto:

Non ci sono file associati a questo prodotto.

Utilizza questo identificativo per citare o creare un link a questo documento:
http://hdl.handle.net/11582/51383

Citazioni

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

CINECA IRIS Institutional Research Information System

Scheda prodotto non validato

File in questo prodotto: