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.
Highlights
Terrain classification is one of the most important applications of PolSAR remote sensing which can provide more information than conventional radar images and greatly improve the ability to discriminate different terrain types
We focus on investigating multifeatures combination and employing a robust classifier named Extremely Randomized Clustering Forests (ERCFs) [18, 19] for terrain classification using PolSAR imagery
The selected POLSAR image has 1236 × 1070 pixels with 8 looks and 30 m×30 m resolution
Summary
Terrain classification is one of the most important applications of PolSAR remote sensing which can provide more information than conventional radar images and greatly improve the ability to discriminate different terrain types. The efforts mainly focus on the following two areas: one is mainly on developing new polarimetric descriptor based on statistical properties and scattering mechanisms; the other is to employ some advanced classifiers originated from machine learning and pattern recognition domain. In 2007, She et al [13] introduced Adaboost for PolSAR image classification; compared with traditional classifiers such as complex Wishart distribution maximum likelihood classifier, these methods are more flexible and robust. Classifiers arise from machine learning and pattern recognition domain such as SVM [15], Adaboost [16], and Random Forests [17] have attracted more attention These methods usually can handle many sophistical image features and usually get remarkable performance.
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