PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery
文献类型：期刊论文
| 作者 | Jiang, Linhuan1,2; Zhang, Zhen1,2; Tang, Bo-Hui1,2,3; Huang, Lehao1,2; Zhang, Bingru1,2 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2024 |
| 卷号 | 17页码:6529-6543 |
| 关键词 | Feature extraction Convolutional neural networks Hyperspectral imaging Data mining Computational modeling Autonomous aerial vehicles Convolution Feature pyramid networks (FPNs) image classification parallel convolutional classification network spectral attention (SA) unmanned aerial vehicle (UAV)-borne hyperspectral imagery |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2024.3370632 |
| 通讯作者 | Zhang, Zhen(zhangzhen@kust.edu.cn) |
| 英文摘要 | Hyperspectral remote sensing images with high spatial resolution (H-2 imagery) have an abundant spatial-spectral information, holding tremendous potential for remote sensing fine-grained monitoring and classification. However, challenges such as high spatial heterogeneity, severe intra-class spectral variability, and poor signal-to-noise ratio especially in unmanned aerial vehicle (UAV) hyperspectral imagery constrain and hinder the performance of fine-grained classification. Convolutional neural network (CNN) emerges as a formidable and excellent tool for image mining and feature extraction, offering effective utility for land cover classification. In this article, a parallel convolutional classification network model based on multimodal filters [including independent component analysis (ICA)-two-dimensional (2-D)-FPN and spectral attention (SA)-3-D-CNN branching structures] PCCN-MSS is proposed for precise H-2 imagery classification. The ICA-2-D-FPN branch integrates ICA into 2-D-CNN to extract the multispatial scale and spectral information of H-2 imagery by feature pyramid networks, meanwhile, the SA-3-D-CNN branch is designed to extract the spatial and spectral information by combining SA mechanism and 3-D-CNN. Taking hyperspectral imagery of UAVs containing vegetation and artifactual material ground as an example, the proposed PCCN-MSS model achieves an overall accuracy of 78.18%, which outperforms by 9.58% to the compared methods. The proposed PCCN-MSS method can mitigate the classification issues of severe salt-and-pepper noise and inaccurate boundary, delivering more satisfactory classification results with robust classification performance and remarkable advantages for H-2 imagery. |
| WOS关键词 | ATTENTION NETWORK ; FUSION |
| 资助项目 | Yunnan Fundamental Research |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001188473800015 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Yunnan Fundamental Research |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/203947]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Zhen |
| 作者单位 | 1.Kunming Univ Sci & Technol, Fac Land Resource Engn, Kunming 650093, Peoples R China 2.Yunnan Prov Dept Educ, Key Lab Plateau Remote Sensing, Kunming 650093, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Linhuan,Zhang, Zhen,Tang, Bo-Hui,et al. PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2024,17:6529-6543. |
| APA | Jiang, Linhuan,Zhang, Zhen,Tang, Bo-Hui,Huang, Lehao,&Zhang, Bingru.(2024).PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,17,6529-6543. |
| MLA | Jiang, Linhuan,et al."PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17(2024):6529-6543. |
入库方式： OAI收割
来源：地理科学与资源研究所
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