A layered sparse adaptive possibilistic approach for hyperspectral image clustering

Author

Xenaki, Spyridoula D. ; Koutroumbas, Konstantinos D. ; Rontogiannis, Athanasios A. ; Sykioti, Olga A.

Author_Institution

IAASARS, Nat. Obs. of Athens, Athens, Greece

fYear

2014

fDate

13-18 July 2014

Firstpage

2890

Lastpage

2893

Abstract

In this paper a new algorithm suitable for hyperspectral image clustering, called L-SAPCM, is proposed. The algorithm works in layers where at each layer, after suitable pre-processing, the SAPCM clustering algorithm ([1]) is applied. Preliminary results on real hyperspectral images show enhanced performance compared to other related methods.

Keywords

geophysical image processing; hyperspectral imaging; image enhancement; pattern clustering; L-SAPCM; SAPCM clustering algorithm; enhanced performance; hyperspectral image clustering; layered sparse adaptive possibilistic approach; real hyperspectral images; Clustering algorithms; Hyperspectral imaging; Roads; Soil; Vectors; Vegetation; hyperspectral; layered clustering; sparsity;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International

Conference_Location

Quebec City, QC

Type

conf

DOI

10.1109/IGARSS.2014.6947080

Filename

6947080

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=143531