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Journal of Applied Remote Sensing • new

Independent component analysis-based band selection techniques for hyperspectral images analysis
Author(s): Rania Zaatour; Sonia Bouzidi; Ezzeddine Zagrouba
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Paper Abstract

Extended multiattribute profiles (EMAPs) are morphological profiles built on the extracted features of a hyperspectral image. These profiles proved, when used in a hyperspectral image classification task, their ability to combine the spectral and spatial information offered by this type of data. We propose building EMAPs on the features selected from a hyperspectral image. To do so, three band selection techniques are proposed. The first one is a modified version of the existent independent component analysis (ICA)-based band selection. The other two are based on the initialization-driven ICA. To test the effectiveness of the aforementioned feature selection methods, we used them to build the EMAPs of hyperspectral images; then, the generated profiles served as the input of two hyperspectral image analysis tasks: a hyperspectral image classification task-based on the sparse representation of EMAPs and an EMAP-based change detection technique that we are proposing in this paper.

Paper Details

Date Published: 14 April 2017
PDF: 22 pages
J. Appl. Remote Sens. 11(2) 026006 doi: 10.1117/1.JRS.11.026006
Published in: Journal of Applied Remote Sensing Volume 11, Issue 2
Show Author Affiliations
Rania Zaatour, Univ. of Tunis El Manar (Tunisia)
Sonia Bouzidi, Univ. de Tunis El Manar (Tunisia)
Ezzeddine Zagrouba, Univ. de Tunis El Manar (Tunisia)

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