In this paper, we present a new methodology for clustering hyperspectral images. It aims at simultaneously solving the following three different issues: 1) estimation of the class statistical parameters; 2) detection of the best discriminative bands without requiring the a priori setting of their number by the user; and 3) estimation of the number of data classes characterizing the considered image. It is formulated within a multiobjective particle swarm optimization (MOPSO) framework and is guided by three different optimization criteria, which are the log-likelihood function, the Bhattacharyya statistical distance between classes, and the minimum description length (MDL). A detailed experimental analysis was conducted on both simulated and real hyperspectral images. In general, the obtained results show that interesting classification performances can be achieved by the proposed methodology despite its completely unsupervised nature.

Clustering of hyperspectral images based on multiobjective particle swarm optimization / Paoli, Andrea; Melgani, Farid; Pasolli, Edoardo. - In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. - ISSN 0196-2892. - 47:12(2009), pp. 4175-4188. [10.1109/TGRS.2009.2023666]

Clustering of hyperspectral images based on multiobjective particle swarm optimization

Pasolli, Edoardo
2009

Abstract

In this paper, we present a new methodology for clustering hyperspectral images. It aims at simultaneously solving the following three different issues: 1) estimation of the class statistical parameters; 2) detection of the best discriminative bands without requiring the a priori setting of their number by the user; and 3) estimation of the number of data classes characterizing the considered image. It is formulated within a multiobjective particle swarm optimization (MOPSO) framework and is guided by three different optimization criteria, which are the log-likelihood function, the Bhattacharyya statistical distance between classes, and the minimum description length (MDL). A detailed experimental analysis was conducted on both simulated and real hyperspectral images. In general, the obtained results show that interesting classification performances can be achieved by the proposed methodology despite its completely unsupervised nature.
2009
Clustering of hyperspectral images based on multiobjective particle swarm optimization / Paoli, Andrea; Melgani, Farid; Pasolli, Edoardo. - In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. - ISSN 0196-2892. - 47:12(2009), pp. 4175-4188. [10.1109/TGRS.2009.2023666]
File in questo prodotto:
File Dimensione Formato  
Paoli_2009.pdf

solo utenti autorizzati

Tipologia: Documento in Post-print
Licenza: Accesso privato/ristretto
Dimensione 2.25 MB
Formato Adobe PDF
2.25 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/732795
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 146
  • ???jsp.display-item.citation.isi??? 122
social impact