We propose a multifeature scheme for terrain classification in SAR image analysis. Different neural classifiers, trained on different features of the same sample space, are combined by using a non-linear ensemble method. The feature extraction modules are chosen in order to discover the textural and contextual characteristics within the neighbourhood of each pixel. Comparisons with classical data fusion techniques and consensus schema are reported. We propose a multifeature scheme for terrain classification in SAR image analysis. Different neural classifiers, trained on different features of the same sample space, are combined by using a non-linear ensemble method. The feature extraction modules are chosen in order to discover the textural and contextual characteristics within the neighbourhood of each pixel. Comparisons with classical data fusion techniques and consensus schema are reported.

Multi-feature adaptive classifiers for SAR image segmentation

Ceccarelli M;
1997

Abstract

We propose a multifeature scheme for terrain classification in SAR image analysis. Different neural classifiers, trained on different features of the same sample space, are combined by using a non-linear ensemble method. The feature extraction modules are chosen in order to discover the textural and contextual characteristics within the neighbourhood of each pixel. Comparisons with classical data fusion techniques and consensus schema are reported. We propose a multifeature scheme for terrain classification in SAR image analysis. Different neural classifiers, trained on different features of the same sample space, are combined by using a non-linear ensemble method. The feature extraction modules are chosen in order to discover the textural and contextual characteristics within the neighbourhood of each pixel. Comparisons with classical data fusion techniques and consensus schema are reported.
File in questo prodotto:
File Dimensione Formato  
Neurocomputing 1997 Ceccarelli.pdf

non disponibili

Licenza: Non specificato
Dimensione 3.74 MB
Formato Adobe PDF
3.74 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: http://hdl.handle.net/20.500.12070/4096
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 30
  • ???jsp.display-item.citation.isi??? ND
social impact