Cognitive Fusion of Graph Neural Network and Convolutional Neural Network for Enhanced Hyperspectral Target Detection | |
Xu, Shufang1,2; Geng, Sijie3; Xu, Pengfei4; Chen, Zhonghao3; Gao, Hongmin1 | |
作者部门 | 光谱成像技术研究室 |
2024 | |
发表期刊 | IEEE Transactions on Geoscience and Remote Sensing
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ISSN | 01962892;15580644 |
卷号 | 62页码:1-15 |
产权排序 | 1 |
摘要 | In recent years, deep learning has emerged as a prominent technique in hyperspectral target detection (HTD). Extensive research has highlighted the potential of graph neural network (GNN) as a promising framework for exploring non-Euclidean dependencies within hyperspectral imagery (HSI). However, GNN has not been introduced to HTD. Additionally, achieving a balanced training set while effectively suppressing background remains a challenge. Therefore, we propose the cognitive fusion of GNN and convolutional neural network (CNN) for enhanced HTD (named as CFGC), which marks the first integration of GNN and CNN in HTD. Initially, using sparse subspace clustering (SSC) and a similarity measurement strategy, we select the most representative background samples for HTD. Subsequently, linear interpolation combines the prior target with the Laplacian-weighted prior target, yielding abundant targets with meaningful transformations. Finally, a fused network of CNN and GNN is utilized for training both the prior target and the constructed training set. Significantly, the incorporation of attention mechanism in both the CNN and GNN branches stands out as a noteworthy advantage, augmenting the models' ability to selectively prioritize crucial information. Four benchmark hyperspectral images have been used in extensive experiments, and the results demonstrate that CFGC exhibits superior performance in HTD. © 1980-2012 IEEE. |
关键词 | Attention mechanism deep learning (DL) graph neural network (GNN) hyperspectral target detection(HTD) sparse subspace clustering (SSC) |
DOI | 10.1109/TGRS.2024.3392188 |
收录类别 | EI |
语种 | 英语 |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
EI入藏号 | 20241815997563 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/97442 |
专题 | 光谱成像技术研究室 |
通讯作者 | Gao, Hongmin |
作者单位 | 1.Hohai University, College of Information Science and Engineering, Changzhou; 213200, China; 2.Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing, Xi'an; 710119, China; 3.Hohai University, College of Computer Science and Software Engineering, Nanjing; 211100, China; 4.Hohai University, Oceanography Institute, Nanjing; 211100, China |
推荐引用方式 GB/T 7714 | Xu, Shufang,Geng, Sijie,Xu, Pengfei,et al. Cognitive Fusion of Graph Neural Network and Convolutional Neural Network for Enhanced Hyperspectral Target Detection[J]. IEEE Transactions on Geoscience and Remote Sensing,2024,62:1-15. |
APA | Xu, Shufang,Geng, Sijie,Xu, Pengfei,Chen, Zhonghao,&Gao, Hongmin.(2024).Cognitive Fusion of Graph Neural Network and Convolutional Neural Network for Enhanced Hyperspectral Target Detection.IEEE Transactions on Geoscience and Remote Sensing,62,1-15. |
MLA | Xu, Shufang,et al."Cognitive Fusion of Graph Neural Network and Convolutional Neural Network for Enhanced Hyperspectral Target Detection".IEEE Transactions on Geoscience and Remote Sensing 62(2024):1-15. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Cognitive Fusion of (4399KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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