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Detecting vehicles is important in aerial surveillance. Traditional methods used classifiers to detect vehicles, but a single classifier was limited to detecting vehicles of only one intensity and orientation. Therefore, the task required the use of multiple classifiers of different intensities and orientations. To solve this problem, we first used a latent Dirichlet allocation (LDA) model that improved...
A novel roof-top extraction method for satellite images based on probabilistic topic model is presented. We model roof-top as the connected structural elements. The proposed method contains two major steps: 1) Detect structural elements, different from earlier structure detector, the proposed method automatically learn the types of elements from unlabeled samples; 2) Connect these elements to form...
Image segmentation can be viewed as the problem of topic assignment, in which pixels are grouped into regions with respect to the topic model of textures and real-world semantic meanings. In this paper, we apply a novel hierarchical topic model, which builds a multi-level image representation covers texture and semantic region, to image segmentation. Specifically, the proposed method firstly assigns...
Textures of land covers provide significant evidences for segmentation and classification. Inspired by resent researches on topic model, we work on a novel texture segmentation method for very high resolution (VHR) remote sensing images based on Latent Dirichlet Allocation (LDA). In order to model spatial relationship between words in LDA, a constraint random variable which is used to control the...
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