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.
Original language | English |
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Title of host publication | Proceedings of IGARSS 2008 |
Subtitle of host publication | International Geoscience and Remote Sensing Symposium : Geoscience and remote sensing, the next generation, 6-11 July 2008 Boston, MA, USA. |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | I-66 - I-69 |
ISBN (Electronic) | 978-1-4244-2808-3 (CD) |
ISBN (Print) | 978-1-4244-2807-6 |
DOIs | |
Publication status | Published - 2008 |
Externally published | Yes |
Event | 2008 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008 - Boston, United States Duration: 6 Jul 2008 → 11 Jul 2008 |
Conference
Conference | 2008 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008 |
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Abbreviated title | IGARSS 2008 |
Country/Territory | United States |
City | Boston |
Period | 6/07/08 → 11/07/08 |
Keywords
- ADLIB-ART-323
- n/a OA procedure