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  • Research Article
  • Open Access

Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests

EURASIP Journal on Advances in Signal Processing20092010:465612

https://doi.org/10.1155/2010/465612

  • Received: 31 May 2009
  • Accepted: 21 October 2009
  • Published:

Abstract

Terrain classification using polarimetric SAR imagery has been a very active research field over recent years. Although lots of features have been proposed and many classifiers have been employed, there are few works on comparing these features and their combination with different classifiers. In this paper, we firstly evaluate and compare different features for classifying polarimetric SAR imagery. Then, we propose two strategies for feature combination: manual selection according to heuristic rules and automatic combination based on a simple but efficient criterion. Finally, we introduce extremely randomized clustering forests (ERCFs) to polarimetric SAR image classification and compare it with other competitive classifiers. Experiments on ALOS PALSAR image validate the effectiveness of the feature combination strategies and also show that ERCFs achieves competitive performance with other widely used classifiers while costing much less training and testing time.

Keywords

  • ALOS
  • Quantum Information
  • Testing Time
  • Image Classification
  • Full Article

Publisher note

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Authors’ Affiliations

(1)
Signal Processing Lab, School of Electronic Information, Wuhan University, Wuhan, 430079, China
(2)
Laboratoire Jean Kuntzmann, CNRS-INRIA, Grenoble University, 51 rue des Mathématiques, 38041 Grenoble, France

Copyright

© Tongyuan Zou et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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