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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...
In this paper, we present a study of extracting urban areas from Polarimetric Synthetic Aperture Radar (PolSAR) images using only positive samples. We solve this problem by learning a standard binary classifier (urban/non-urban) given an incomplete set of positive samples (urban) and a set of unlabeled samples (some of which are urban and some of which are non-urban) based on the work of Elkan and...
A hierarchical boosting algorithm based on feature selection is proposed for Synthetic Aperture Radar (SAR) image retrieval here. Motivated by Joint Boost and Shared feature frameworks, category combinations are adopted as the training and classification set of a hierarchical boosting-based classification frameworkpsilas middle layer. It has superiorities over the classical method which combines Boosting...
This paper describes a three-step algorithm for automatic extraction of power transmission tower series in full polarimetric SAR imagery. Firstly, the method uses a polarimetric whitening filter to combine the information in different polarimetric channels and reduces the speckle. Then the point-like targets are detected by an adaptive region classification CFAR detection approach, among which only...
In this, paper, we propose an improved classification algorithm which is based on the four-Component scattering model. Compared with the three-component model introduced by Freeman and Durden, the four-component scattering model introduces the "helix scattering" as its fourth component. Our algorithm emphasizes the existence of pixels with mixed scattering mechanism, and applies the result...
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