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A novel approach to the selection of spatially 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

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C3F9-0
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.