HSI-TransUNet: A transformer based semantic segmentation model for crop mapping from UAV hyperspectral imagery
文献类型：期刊论文
| 作者 | Niu, Bowen1; Feng, Quanlong1; Chen, Boan1; Ou, Cong2; Liu, Yiming3; Yang, Jianyu1 |
| 刊名 | COMPUTERS AND ELECTRONICS IN AGRICULTURE
 |
| 出版日期 | 2022-10-01 |
| 卷号 | 201页码:15 |
| 关键词 | UAV Hyperspectral remote sensing Deep learning Crop classification |
| ISSN号 | 0168-1699 |
| DOI | 10.1016/j.compag.2022.107297 |
| 通讯作者 | Feng, Quanlong(fengql@cau.edu.cn) |
| 英文摘要 | UAV hyperspectral imagery (HSI) has the unique merits of both a very high spatial and spectral resolution, which provides a high-quality data source for automatic crop mapping. Recently, deep learning has been widely used in crop classification, however, the design of an accurate crop mapping model for HSI data still remains a chal-lenging task. Therefore, this paper aims to propose a novel semantic segmentation model (HSI-TransUNet) for crop mapping, which could make full use of the abundant spatial and spectral information of UAV HSI data simultaneously. Specifically, the proposed HSI-TransUNet belongs to an improved version of TransUNet, and we have made four important modifications for HSI data. Firstly, a spectral-feature attention module is designed for spectral features aggregation in the encoder. Afterwards, a series of Transformer layers with residual connections are designed to learn global contextual features. In the decoder part, sub-pixel convolutions are adopted to avoid the chess-board effect in the segmentation results. Finally, we design a hybrid loss function to further refine the predictions for boundaries. Experiment results indicate that the proposed HSI-TransUNet has achieved good performance in crops identification with an overall accuracy of 86.05%. Ablation studies have been conducted to verify the effectiveness of each refined module in the HSI-TransUNet. Comparison experiments also show that HSI-TransUNet has outperformed several previous semantic segmentation models. The dataset in this paper, UAV-HSI-Crop, is publicly available. http://doi.org/10.57760/sciencedb.01898. |
| 资助项目 | National Natural Science Foundation of China[42001367] ; National Key Research and Development Pro-gram of China[2021YFE0102300] |
| WOS研究方向 | Agriculture ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000849494400001 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Pro-gram of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/182337]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Feng, Quanlong |
| 作者单位 | 1.China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.China Mobile Grp Guangdong Co Ltd, Guangzhou 510623, Peoples R China |
| 推荐引用方式 GB/T 7714 | Niu, Bowen,Feng, Quanlong,Chen, Boan,et al. HSI-TransUNet: A transformer based semantic segmentation model for crop mapping from UAV hyperspectral imagery[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2022,201:15. |
| APA | Niu, Bowen,Feng, Quanlong,Chen, Boan,Ou, Cong,Liu, Yiming,&Yang, Jianyu.(2022).HSI-TransUNet: A transformer based semantic segmentation model for crop mapping from UAV hyperspectral imagery.COMPUTERS AND ELECTRONICS IN AGRICULTURE,201,15. |
| MLA | Niu, Bowen,et al."HSI-TransUNet: A transformer based semantic segmentation model for crop mapping from UAV hyperspectral imagery".COMPUTERS AND ELECTRONICS IN AGRICULTURE 201(2022):15. |
入库方式： OAI收割
来源：地理科学与资源研究所
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