Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests
Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests
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DOI：
10.1155/2010/465612
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发表时间：
2010
影响因子：
1.9
通讯作者：
Tongyuan Zou;Wen Yang;Dengxin Dai;Hong Sun
中科院分区：
文献类型：
--
作者：
Tongyuan Zou;Wen Yang;Dengxin Dai;Hong Sun
利用极化SAR图像进行地形分类是近年来一个非常活跃的研究领域。虽然已经提出了很多的特征和许多分类器已被采用，有很少的工作比较这些特征及其组合与不同的分类器。在本文中，我们首先评估和比较不同的功能分类极化SAR图像。然后，我们提出了两种策略的特征组合：手动选择根据启发式规则和自动组合的基础上，一个简单而有效的标准。最后，我们引入极端随机聚类森林（ERCFs）极化SAR图像分类，并与其他竞争对手的分类器进行比较。在ALOS PALSAR图像上的实验验证了特征组合策略的有效性，同时也表明ERCFs在训练和测试时间上与其他广泛使用的分类器相比具有竞争力的性能。
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.