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In this paper, we attempt to solve the efficiency problem of PolSAR scene classification with non-parametric classifier. We employ the tree-structure based search strategy to perform fast approximate nearest neighbour search by introducing the multiple randomized kd-tree and hierarchical kmeans-tree into ONBNN classifier. The experimental results on RadarSat-2 PolSAR dataset demonstrate that our method...
We compare the scene classification performance of 13 features, including structure, texture and color features. First, image classification are performed using a single feature and the performance of different features are compared. Both the k-nearest-neighbor (KNN) classifier and the support vector machine classifier (SVM) are employed. And for the KNN classifier, we use four different distance...
This paper presents a new method for satellite image classification. Specifically, we make two main contributions: (1) we introduce the sparse coding method for high-resolution satellite image classification; (2) we effectively combine a set of diverse and complementary features-SIFT, Color Histogram and Gabor to further improve the performance. A two-stage linear SVM classifier is designed for this...
This paper presents an evaluation of different features for polarimetric SAR (PolSAR) image classification. Firstly, we select several of the polarimetric features to give a summary on them. Then we give an insight into their classification performance together with a texture feature using the support vector machine (SVM). Finally, we employ a feature combination and selection strategy that optimizes...
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