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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...
This paper discusses the difficulties and problems in the study of gearbox fault diagnosis currently, and moreover recommends a method to deal with non-linear model equations, non-Gaussian distribution noise of the fault diagnosis problems using particle filtering (PF) techniques. In this paper, By summarizing the current research situation and tendency both at home and abroad, it puts forward the...
To solve multi-class problems of support vector machines (SVM) more efficiently, a novel framework, which we call class-incremental learning (CIL), is proposed in this paper. CIL consists of two phases: incremental feature selection and incremental training, for updating the knowledge of old SVM classifiers in text classification when new classes are added to the system. CIL reuses the old models...
In this paper, a novel approach for change detection in multitemporal synthetic aperture radar (SAR) images is presented. The proposed approach based on region likelihood ratio feature detection exploits an edge fusion technique for the SAR images segmentation. Segmentation is the key process in change detection based on region feature and the proposed edge fusion technique of two segmentation images...
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