Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests. (13th December 2009)

• Record Type: Journal Article
• Authors: Zou Zou, Tongyuan Tongyuan; Yang Yang, Wen Wen; Dai Dai, Dengxin Dengxin; Sun Sun, Hong Hong
• Other Names: Lopez-Martinez Lopez-Martinez Carlos Carlos Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2009-12-13
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/465612
• Languages: English
• ISSNs: 1687-6172

Abstract

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.