A Novel Approach to the Selection of Robust and Invariant Features for 
Classification of Hyperspectral Images

Bruzzone, L; Persello, C

doi:10.1109/IGARSS.2008.4778794

Datensatz

DATENSATZ AKTIONENEXPORT

Zur Ablage hinzufügen

Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben

Konferenzbeitrag

A Novel Approach to the Selection of Robust and Invariant Features for Classification of Hyperspectral Images

MPG-Autoren

Es sind keine MPG-Autoren in der Publikation vorhanden

Externe Ressourcen

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4778794
(Verlagsversion)

Volltexte (beschränkter Zugriff)

Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.

Volltexte (frei zugänglich)

Es sind keine frei zugänglichen Volltexte in PuRe verfügbar

Ergänzendes Material (frei zugänglich)

Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar

Zitation

Bruzzone, L., & Persello, C. (2009). A Novel Approach to the Selection of Robust and Invariant Features for Classification of Hyperspectral Images. In IGARSS 2008: 2008 IEEE International Geoscience and Remote Sensing Symposium (pp. I-66-I-69). Piscataway, NJ, USA: IEEE. doi:10.1109/IGARSS.2008.4778794.

Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-C833-F

Zusammenfassung

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.