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In this paper, a novel iterative clustering based active learning (ICAL) method for hyperspectral image classification is proposed. On the one hand, the extreme learning machine is combined with the Markov random field (ELM-MRF) for label assignment, to exploit both spectral and spatial information to boost classification result. On the other hand, an iterative clustering based sample selection strategy...
In this paper, a decision fusion of pixel-level and superpixel-level classifiers (DFPSC) for the HSI is proposed. First, the support vector machine based classification probability combined with the local spatial information is introduced to classify the HSI in a pixel-by-pixel manner. Then, the HSI is over-segmented into non-overlapping superpixels. Each superpixel contains spatially-connected and...
In this paper, a gradient-guided sparse representation method (GGSR) for the hyperspectral image denoising is proposed. In the context of the hyperspectral image, neighbourhood spectral bands always have highly similar spatial and structural characteristics, which can be jointly used to improve the image quality. On the one hand, the sparse representation, as one powerful image processing tool, is...
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