高光谱成像
图形
邻接表
模式识别(心理学)
计算机科学
人工智能
分割
像素
卷积(计算机科学)
算法
人工神经网络
理论计算机科学
作者
Zhaohui Xue,Zhiwei Liu,Mengxue Zhang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-18
被引量:8
标识
DOI:10.1109/tgrs.2023.3253248
摘要
Graph convolution networks (GCNs) have shown great potentials for few-shot hyperspectral image (HSI) classification. Mainstream GCNs construct graph according to single scale segmentation, which usually ignores subtle adjacency relation between small regions, leading to unreliable initial local graph. To overcome the above issue, we propose a differentiated-scale restricted GCN (DSR-GCN) for HSI classification. Firstly, we propose a differentiated-scale graph construction method considering both the subtle and relative wider range spectral-spatial relation. Secondly, restricted fusion loss is designed to restrict the fusion of features extracted with differentiated-scale GCN branches. Finally, we design a lightweight spatial-spectral siamese network to remedy local pixel-level features. The proposed DSR-GCN can better model spatial structure with a reliable and refined graph, and it can capture more discriminate features in few-shot learning (FSL) scenario. Extensive experiments conducted on four benchmark data sets demonstrate that DSR-GCN outperforms the other deep learning methods in terms of classification accuracy and generalization performance, with the improvements in terms of OA around 6.20%~23.41% (Indian Pines), 4.45%~ 16.48% (University of Pavia), 4.25%~11.85% (Salinas), and 2.0%~17.23% (University of Houston) under 5 labeled samples per class.
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