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A rough set approach to the discovery of classification rules in spatial data Yee Leung a , Tung Fung a , Ju‐Sheng Mi b & Wei‐Zhi Wu c a Department of Geography and Resource Management, Center for Environmental Policy and Resource Management, and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong b College of(More)
A new method, which is called decomposition-composition (D-C) method, is proposed for the nonlinear dimensionality reduction (NLDR) of data lying on the multicluster manifold. The main idea is first to decompose a given data set into clusters and independently calculate the low-dimensional embeddings of each cluster by the decomposition procedure. Based on(More)
Geodesic distance estimation for data lying on a manifold is an important issue in many applications of nonlinear dimensionality reduction. In this paper, a method aiming at improving the precision of geodesic distance estimation is proposed. The method is constructed on the basic principle, locally linear assumption, underlying the manifold data. It(More)
Image fusion at pixel level without precise registration always causes pseudo colors and other problem. Classification-based fusion scheme can effectively eliminate the false color at the edge of objective. However, the traditional per-pixel classification results in the well-known salt and pepper effect. The only way to smooth the image is to use filters,(More)
Feature extraction has been a major area of research in remote sensing, and fractal feature is a natural characterization of complex objects across scales. Extending on the modified triangular prism (MTP) method, we systematically discuss three factors closely related to the estimation of fractal dimensions of remotely sensed images. They are namely the(More)
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