Hyperspectral Image Classification Based on Multibranch Adaptive Feature Fusion Network
文献类型：期刊论文
| 作者 | Li, Chen; Wang, Yi2; Fang, Zhice; Li, Penglei |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2024-10-01 |
| 卷号 | 62页码:5528318 |
| 关键词 | Feature extraction Hyperspectral imaging Data mining Three-dimensional displays Convolution Interference Convolutional neural networks Convolutional neural networks (CNNs) feature fusion hyperspectral image classification (HSIC) multibranch |
| DOI | 10.1109/TGRS.2024.3449878 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Convolutional neural networks (CNNs) are widely used in hyperspectral image classification (HSIC) due to their exceptional performance. However, current methods for multiscale feature extraction typically rely on single-branch CNNs, potentially causing interference among features of varying scales. To mitigate this issue, we present a multibranch adaptive feature fusion network (MBAFFN) classification method. MBAFFN enhances feature uniqueness and improves the accuracy and reliability of classification results by extracting information at multiple scales through three parallel branches. Furthermore, to address the challenge of capturing global features within CNNs, we introduce a global detail attention (GDA) mechanism aimed at bolstering the network's capability to capture comprehensive information. In addition, we mitigate the issue of neglecting center-pixel importance in convolution operations through a distance suppression attention (DSA) design. To effectively integrate outcomes from multiple branches, we propose a pixel-based adaptive feature fusion strategy, thereby increasing the proportion of features conducive to improved classification results. Lastly, auxiliary loss functions are employed to train the multibranch network. Experimental results on four benchmark datasets demonstrate the superiority of our approach over several state-of-the-art methods, particularly in managing imbalanced small samples. Furthermore, ablation studies validate the effectiveness of the proposed modules. |
| WOS关键词 | SPECTRAL-SPATIAL CLASSIFICATION ; ZERO-SHOT ; ATTENTION |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001308252000024 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/208024]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wang, Yi |
| 作者单位 | 1.State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.China Univ Geosci, Sch Geophys & Geomat, Wuhan 430074, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Chen,Wang, Yi,Fang, Zhice,et al. Hyperspectral Image Classification Based on Multibranch Adaptive Feature Fusion Network[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:5528318. |
| APA | Li, Chen,Wang, Yi,Fang, Zhice,&Li, Penglei.(2024).Hyperspectral Image Classification Based on Multibranch Adaptive Feature Fusion Network.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,5528318. |
| MLA | Li, Chen,et al."Hyperspectral Image Classification Based on Multibranch Adaptive Feature Fusion Network".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):5528318. |
入库方式： OAI收割
来源：地理科学与资源研究所
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