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Article Dans Une Revue EURASIP Journal on Advances in Signal Processing Année : 2010

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

Résumé

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.

Dates et versions

hal-00851734 , version 1 (17-08-2013)

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Tongyuan Zou, Wen Yang, Dengxin Dai, Hong Sun. Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests. EURASIP Journal on Advances in Signal Processing, 2010, 2010, pp.Article No. 4. ⟨10.1155/2010/465612⟩. ⟨hal-00851734⟩
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