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Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network
2017
Remote Sensing Letters
In this paper, a novel dual-channel convolutional neural network (DC-CNN) framework is proposed for accurate spectral-spatial classification of hyperspectral image (HSI). In this framework, one-dimensional CNN (1D CNN) is utilized to automatically extract the hierarchical spectral features and two-dimensional CNN (2D CNN) is applied to extract the hierarchical space-related features, and then a softmax regression classifier is used to combine the spectral and spatial features together and
doi:10.1080/2150704x.2017.1280200
fatcat:k2wivulba5fancunj6lobzdecm