Identifying and mapping individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning
文献类型：期刊论文
| 作者 | Zhang, Ce1,2; Atkinson, Peter M.3; George, Charles4; Wen, Zhaofei5 ; Diazgranados, Mauricio6; Gerard, France4
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| 刊名 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
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| 出版日期 | 2020-11-01 |
| 卷号 | 169页码:280-291 |
| 关键词 | Multi-scale deep learning Residual U-Net Scale sequence Semantic segmentation Paramos |
| ISSN号 | 0924-2716 |
| DOI | 10.1016/j.isprsjprs.2020.09.025 |
| 通讯作者 | Zhang, Ce(c.zhang9@lancaster.ac.uk) ; Atkinson, Peter M.(pma@lancaster.ac.uk) |
| 英文摘要 | The identification and counting of plant individuals is essential for environmental monitoring. UAV based imagery offer ultra-fine spatial resolution and flexibility in data acquisition, and so provide a great opportunity to enhance current plant and in-situ field surveying. However, accurate mapping of individual plants from UAV imagery remains challenging, given the great variation in the sizes and geometries of individual plants and in their distribution. This is true even for deep learning based semantic segmentation and classification methods. In this research, a novel Scale Sequence Residual U-Net (SS Res U-Net) deep learning method was proposed, which integrates a set of Residual U-Nets with a sequence of input scales that can be derived automatically. The SS Res U-Net classifies individual plants by continuously increasing the patch scale, with features learned at small scales passing gradually to larger scales, thus, achieving multi-scale information fusion while retaining fine spatial details of interest. The SS Res U-Net was tested to identify and map frailejones (all plant species of the subtribe Espeletiinae), the dominant plants in one of the world's most biodiverse high-elevation ecosystems (i.e. the paramos) from UAV imagery. Results demonstrate that the SS Res U-Net has the ability to self-adapt to variation in objects, and consistently achieved the highest classification accuracy (91.67% on average) compared with four state-of-the-art benchmark approaches. In addition, SS Res U-Net produced the best performances in terms of both robustness to training sample size reduction and computational efficiency compared with the benchmarks. Thus, SS Res U-Net shows great promise for solving remotely sensed semantic segmentation and classification tasks, and more general machine intelligence. The prospective implementation of this method to identify and map frailejones in the paramos will benefit immensely the monitoring of their populations for conservation assessments and management, among many other applications. |
| 资助项目 | Centre of Excellence in Environmental Data Science (CEEDS) ; NERC/AHRC[NE/R017654/1] |
| WOS研究方向 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000584231200022 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/12386]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Zhang, Ce; Atkinson, Peter M. |
| 作者单位 | 1.Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England 2.UK Ctr Ecol & Hydrol, Lib Ave, Lancaster LA1 4AP, England 3.Univ Lancaster, Fac Sci & Technol, Lancaster LA1 4YR, England 4.UK Ctr Ecol & Hydrol, Maclean Bldg,Benson Lane, Wallingford OX10 8BB, Oxon, England 5.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Key Lab Reservoir Aquat Environm, Chongqing 400714, Peoples R China 6.Royal Bot Gardens, Ardingly RH17 6TN, W Sussex, England |
| 推荐引用方式 GB/T 7714 | Zhang, Ce,Atkinson, Peter M.,George, Charles,et al. Identifying and mapping individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2020,169:280-291. |
| APA | Zhang, Ce,Atkinson, Peter M.,George, Charles,Wen, Zhaofei,Diazgranados, Mauricio,&Gerard, France.(2020).Identifying and mapping individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,169,280-291. |
| MLA | Zhang, Ce,et al."Identifying and mapping individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 169(2020):280-291. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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