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Spectral-spatial classification of hyperspectral images using Markov random fields (MRFs) has been demonstrated effective to improve the classification accuracy. However, it still suffers from the little availability of labeled samples. In order to collect the most informative samples and train an effective classifier, active learning (AL) methods are often used. In this paper, we present a new AL...
With the complex building composition and imaging condition, urban areas show versatile characteristics in remote sensing images. In the literature of land-cover analysis, many algorithms utilize the features with structural information to characterize urban areas. Typically, these are more successful on some types of imagery than others, since they usually use only one kind or a few kinds of structural...
Many approaches for object detection are based on Markov random field (MRF) and conditional random field (CRF) respectively. MRF and CRF have very different characteristics. This work discusses in detail their strength and weaknesses. From the discussion, a new object detection algorithm using combination of CRF and MRF was derived. We utilize the algorithm to detect urban areas, and corresponding...
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