The nature of statistical learning theory
The nature of statistical learning theory
Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Machine Learning
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.