**Hyperspectral Image Classification Based on a Shuffled Group Convolutional Neural Network with Transfer Learning**

**Yao Liu 1, Lianru Gao 2,\*, Chenchao Xiao 1, Ying Qu 3, Ke Zheng <sup>2</sup> and Andrea Marinoni <sup>4</sup>**


This paper proposed a novel, lightweight, shuffled group convolutional neural network (abbreviated as SG-CNN) to achieve efficient training with a limited training dataset in HSI classification. It consists of SG conv units that employ conventional and atrous convolution in different groups, followed by channel shuffle operation and shortcut connection. As a result, SG-CNNs have less trainable parameters, whilst they can still be accurately and efficiently trained with fewer labeled samples. In addition, transfer learning between different HIS datasets was also applied to the SG-CNN to further improve the classification accuracy. The experimental results demonstrated that SG-CNNs can achieve a competitive classification performance when the amount of labeled data for training is poor, as well as efficiently provide satisfying classification results.

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