A Novel Approach to the Selection of Robust and Invariant Features for 
Classification of Hyperspectral Images

Bruzzone, L; Persello, C

doi:10.1109/IGARSS.2008.4778794

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A Novel Approach to the Selection of Robust and Invariant Features for Classification of Hyperspectral Images

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4778794
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Bruzzone, L., & Persello, C. (2009). A Novel Approach to the Selection of Robust and Invariant Features for Classification of Hyperspectral Images. In IGARSS 2008: 2008 IEEE International Geoscience and Remote Sensing Symposium (pp. I-66-I-69). Piscataway, NJ, USA: IEEE. doi:10.1109/IGARSS.2008.4778794.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C833-F

Abstract

This paper presents a novel approach to feature selection for the classification of hyperspectral images. The proposed approach aims at selecting a subset of the original set of features that exhibits two main properties:( i) high capability to discriminate among the considered classes, (ii) high invariance (stationarity) in the spatial domain of the investigated scene. The feature selection is accomplished by defining a multi-objective criterion that considers two terms: (i) a term that assesses the class separability, (ii) a term that evaluates the spatial invariance of the selected features. The multi-objective problem is solved by an evolutionary algorithm that estimates the Pareto-optimal solutions. Experiments carried out on a hyperspectral image acquired by the Hyperion sensor confirmed the effectiveness of the proposed technique.