**Deep Relation Network for Hyperspectral Image FewȬShot Classification**

**Kuiliang Gao 1,\*, Bing Liu 1, Xuchu Yu 1, Jinchun Qin 2, Pengqiang Zhang <sup>1</sup> and Xiong Tan <sup>1</sup>**

<sup>1</sup> Information Engineering University, Zhengzhou 450001, China

<sup>2</sup> Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China

\* Correspondence: 311405000803@home.hpu.edu.cn

This paper developed a few-shot hyperspectral images classification approach using only a few labeled samples. It consists of two modules, i.e., a feature learning module and a relation learning module to capture the spatial–spectral information in hyperspectral images and then carry out relation learning by comparing the similarity between samples. It is followed by a task-based learning strategy to enhance its ability in terms of learning with a large number of tasks randomly generated from different data sets. Accordingly, the proposed method has excellent generalization ability and can achieve satisfactory classification with only a few labeled samples. The experimental results indicated that the proposed method can perform better than the traditional, semisupervised support vector machine and semisupervised deep learning models.

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