Liu, Yao; Gao, Lianru; Xiao, Chenchao; Qu, Ying; Zheng, Ke; Marinoni, Andrea (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-06-01)
Convolutional neural networks (CNNs) have been widely applied in hyperspectral imagery
(HSI) classification. However, their classification performance might be limited by the scarcity of
labeled data to be used for training and validation. In this paper, we propose a novel lightweight
shuffled group convolutional neural network (abbreviated as SG-CNN) to achieve efficient training
with a limited ...