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Conference Paper

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

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84133

Persello,  C
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Bruzzone, L., & Persello, C. (2008). A Novel Approach to the Selection of Robust and Invariant Features for Classification of Hyperspectral Images. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2008) (pp. I-66-I-69). Piscataway, NJ, USA: IEEE.


Cite as: http://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.