A Novel Approach to the Selection of Robust and Invariant Features for Classification of Hyperspectral Images
2008
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 (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.
Author(s): | Bruzzone, L. and Persello, C. |
Pages: | I-66-I-69 |
Year: | 2008 |
Month: | July |
Day: | 0 |
Publisher: | IEEE |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
DOI: | 10.1109/IGARSS.2008.4778794 |
Event Name: | IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2008) |
Event Place: | Boston, MA , USA |
Address: | Piscataway, NJ, USA |
Digital: | 0 |
ISBN: | 978-1-4244-2807-6 |
Links: |
Web
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BibTex @inproceedings{BruzzoneP2008_2, title = {A Novel Approach to the Selection of Robust and Invariant Features for Classification of Hyperspectral Images }, author = {Bruzzone, L. and Persello, C.}, pages = {I-66-I-69 }, publisher = {IEEE}, address = {Piscataway, NJ, USA}, month = jul, year = {2008}, doi = {10.1109/IGARSS.2008.4778794 }, month_numeric = {7} } |