A novel approach to the selection of spatially invariant features for 
classification of hyperspectral images

Persello, C

doi:10.1109/IGARSS.2009.5418001

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A novel approach to the selection of spatially invariant features for classification of hyperspectral images

Persello, C. (2009). A novel approach to the selection of spatially invariant features for classification of hyperspectral images. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009) (pp. II-61-II-64). Piscataway, NJ, USA: IEEE.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C3F9-0 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C3FA-E

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Persello, C¹, Author

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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795

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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 in the spatial domain of the investigated scene. This approach results in a more robust classification system with improved generalization properties with respect to standard feature-selection methods. The feature selection is accomplished by defining a multi-objective criterion function made up of two terms: i) a term that measures the class separability, ii) a term that evaluates the spatial invariance of the selected features. In order to assess the spatial invariance of the feature subset we propose both a supervised method and a semisupervised method (which choice depends on the available reference data). The multi-objective problem is solved by an evolutionary algorithm that estimates the set of Pareto-optimal solutions. Experiments carried out on a hyperspectral image acquired by the Hyperion sensor on a complex area confirmed the effectiveness of the proposed approach.

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Dates: Date issued: 2009-07

Publication Status: Issued

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Identifiers: ISBN: 978-1-4244-3394-0
URI: http://www.igarss09.org/
DOI: 10.1109/IGARSS.2009.5418001
BibTex Citekey: PerselloB2009

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Title: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009)

Place of Event: Cape Town, South Africa

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Title: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009)

Source Genre: Proceedings

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Publ. Info: Piscataway, NJ, USA : IEEE

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: II-61-II-64 Identifier: -