Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 465612 9 pages doi 2010 465612 Research Article Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests Tongyuan Zou 1 Wen Yang 1 2 Dengxin Dai 1 and Hong Sun1 1 Signal Processing Lab School of Electronic Information Wuhan University Wuhan 430079 China 2Laboratoire Jean Kuntzmann CNRS-INRIA Grenoble University 51 rue des Mathematiques 38041 Grenoble France Correspondence should be addressed to Wen Yang yangwen@ Received 31 May 2009 Revised 4 October 2009 Accepted 21 October 2009 Academic Editor Carlos Lopez-Martinez Copyright 2010 Tongyuan Zou et al. 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. 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. 1. Introduction Terrain classification is one of the most important applications
