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

被引：355

作者：

Dalla Mura, Mauro ^{[1
,2
]}

Villa, Alberto ^{[2
,3
]}

Benediktsson, Jon Atli ^{[2
]}

Chanussot, Jocelyn ^{[3
]}

Bruzzone, Lorenzo ^{[1
]}

机构：

[1] Univ Trent, Dept Informat Engn & Comp Sci, I-38100 Trento, Italy

[2] Univ Iceland, Fac Elect & Comp Engn, IS-107 Reykjavik, Iceland

[3] Grenoble Inst Technol, Grenoble Images Parole Signals Automat Lab, Signal & Image Dept, F-38402 St Martin Dheres, France

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2011年 / 8卷 / 03期

关键词：

Attribute filters; decision fusion; extended attribute profile (EAP); independent component analysis (ICA); mathematical morphology; remote sensing;

D O I：

10.1109/LGRS.2010.2091253

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

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.

引用

页码：542 / 546

页数：5

共 13 条

[1]

[Anonymous], P IEEE INT GEOSC REM

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