A novel approach to the selection of spatially invariant features for the classification of hyperspectral images with improved generalization capability

Lorenzo Bruzzone, Claudio Persello

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114 Citations (Scopus)
3 Downloads (Pure)

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 at the same time high capability to discriminate among the considered classes and 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 multiobjective criterion function made up of two terms: (1) a term that measures the class separability and (2) 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 (which assumes that training samples acquired in two or more spatially disjoint areas are available) and a semisupervised method (which requires only a standard training set acquired in a single area of the scene and takes advantage of unlabeled samples selected in portions of the scene spatially disjoint from the training set). The choice for the supervised or semisupervised method depends on the available reference data. The multiobjective 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.
Original languageEnglish
Pages (from-to)3180-3191
JournalIEEE transactions on geoscience and remote sensing
Volume47
Issue number9
DOIs
Publication statusPublished - 2009

Keywords

  • ADLIB-ART-2875
  • n/a OA procedure

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

    Persello, C. & Bruzzone, L., 2009, IGARSS 2009: Proceedings IEEE International Geoscience & Remote Sensing Symposium: Earth observation, origins to applications, July 12-17, 2009, Cape Town, South Africa. Piscataway, NJ: IEEE, p. II-61 - II-64 (IEEE International Geoscience and Remote Sensing Symposium (IGARSS); vol. 2009).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    4 Citations (Scopus)

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