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

2009

Conference Paper

ei


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.

Author(s): Persello, C. and Bruzzone, L.
Pages: II-61-II-64
Year: 2009
Month: July
Day: 0
Publisher: IEEE

Department(s): Empirische Inferenz
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/IGARSS.2009.5418001
Event Name: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009)
Event Place: Cape Town, South Africa

Address: Piscataway, NJ, USA
Digital: 0
ISBN: 978-1-4244-3394-0

Links: Web

BibTex

@inproceedings{PerselloB2009,
  title = {A novel approach to the selection of spatially invariant features for classification of hyperspectral images },
  author = {Persello, C. and Bruzzone, L.},
  pages = {II-61-II-64 },
  publisher = {IEEE},
  address = {Piscataway, NJ, USA},
  month = jul,
  year = {2009},
  month_numeric = {7}
}