Header logo is ei

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

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},
  month_numeric = {7}
}