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In this letter, we present a novel approach for spectral–spatial classification in hyperspectral imagery. After applying principal component (PC) analysis for dimensionality reduction, we extract the spectral–spatial information by first reorganizing the local image patch with the first PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation...
Support Vector Machines (SVM) for image classification proved to perform well in many applications. However, they are often not preferred in hyperspectral image analysis due to long processing times caused by a high number of support vectors and large data sets. We present two approaches that speed-up the classification process with SVM by a) simplifying the original SVM, i.e. reducing the number...
Classification is an important technique of hyperspetral imagery processing. In traditional classification methods of hyperspectral imagery, all classes are treated equally. Some of them, however, should be given more regard, and so, it is significant to emphasize particularly on the analysis effect of classes of interest. In this case, two kinds of processing methods are proposed to protect classes...
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