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Intra-class Similarity Structure Representation based Hyperspectral Imagery Classification with Few Samples
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral imagery (HSI) classification is one of the fundamental applications in remote sensing domain, which aims at predicting the labels of unlabeled pixels in an image with a classifier trained on a certain amount of labeled pixels. However, due to the expensive cost on manual labeling, only limited labeled pixels can be obtained in real applications, which is prone to result in the training of classifier to be overfitting. To address this problem, we present an intraclass similarity
doi:10.1109/jstars.2020.2977655
fatcat:3f6p4sfizra6phu3cozbclovri