近日，广东技术师范大学数据智能与感知计算实验室研究生董子阳等人论文被Remote sensing（IF：5.349，SCI二区top）期刊录用。论文的题目是H-RNet: Hybrid Relation Network for Few-Shot Learning-Based Hyperspectral Image Classification（http://doi.org/10.3390/rs15102497），以下是论文的简要分享：
【Abstract】Deep network models rely on sufficient training samples to perform reasonably well, which has inevitably constrained their application in the classification of hyperspectral images (HSIs) due to the limited availability of labeled data. To tackle this particular challenge, we propose a hybrid relation network, H-RNet, by combining three-dimensional (3-D) convolution neural networks (CNN) and two-dimensional (2-D) CNN to extract the spectral–spatial features whilst reducing the complexity of the network. In an end-to-end relation learning module, the sample pairing approach can effectively alleviate the problem of few labeled samples and learn correlations between samples more accurately for more effective classification. Experimental results on three publicly available datasets have fully demonstrated the superior performance of the proposed model in comparison to a few state-of-the-art methods.
Keywords：
HSI classification; few-shot learning; relation network; transfer learning
实验结果表明，本文的H-RNet方法可以更好地从高维数据中提取空间和光谱信息，在高维图像分类任务中具有更高的准确性和鲁棒性。
本研究由国家自然科学基金项目(No.62172113、62006049和61906216)，教育部人文社会科学专项(No.18JDGC012)，广东省知识产权与大数据重点实验室项目(No.2018B030322016)，广东省科技专项(No.KTP20210197&No.2017A040403068)，广东省教育厅项目(No.2022KTSCX068)，广东技术师范大学资助项目(No.530992),广东省基础与应用基础研究基金(No.2023a1515010939)的资助。
详细报道，请访问 https://mp.weixin.qq.com/s/njAL1CyAA40GD6XgfWmSXA
