期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 8, 期 3, 页码 542-546出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2010.2091253
关键词
Attribute filters; decision fusion; extended attribute profile (EAP); independent component analysis (ICA); mathematical morphology; remote sensing
类别
资金
- European community [MRTN-CT-2006-035927]
- University of Iceland
- University of Trento
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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