中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
  4. 资源与环境信息系统国家重点实验室
  5. 外文论文
Individual Populus euphratica Tree Detection in Sparse Desert Forests Based on Constrained 2-D Bin Packing

文献类型：期刊论文

作者Wang, Haoyu3,4,5,6,7,8; Li, Junli4,8; Van de Voorde, Tim3,5,7,8; Zhou, Chenghu2,6; De Maeyer, Philippe3,5,7,8; Ma, Yubo1,6; Shen, Zhanfeng1,6
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2024
卷号62页码:19
关键词Forests Filling Semantic segmentation Feature extraction Clustering algorithms Random forests Geography Individual tree detection Populus euphratica (P. euphratica) semantic segmentation template filling
ISSN号0196-2892
DOI10.1109/TGRS.2024.3391352
英文摘要Detecting individual Populus euphratica (P. euphratica) trees in desert forest areas is crucial for monitoring their ecophysiological characteristics and ecological conservation. However, the presence of the spectral-similar Tamarix chinensis (T. chinensis) in the habitats, along with the densely overlapping crowns in clustered P. euphratica, presents a challenge for the task. This article proposes a method to detect individual P. euphratica in very high spatial resolution (VHR) images. First, the deep learning-based semantic segmentation model is used to differentiate between P. euphratica and T. chinensis. Second, the individual tree detection is converted into a constrained 2-D bin packing model and solved by a heuristic template matching and filling algorithm. The experimental data consist of a WorldView-2 image capturing sparse desert forests of the lower reaches of the Tarim River. The 22 296 individual P. euphratica trees were detected, achieving F1-scores of 0.885, 0.869, and 0.902 on three datasets with varying difficulty levels. Furthermore, experiments were conducted to compare with other methods, and the results showed that the proposed method achieved the best performance on all three datasets. The proposed method can be applied to map the distribution of individual P. euphratica trees in sparse desert forests and can provide methodological references for similar tasks related to individual tree detection in natural forests.
WOS关键词CROWN DETECTION ; SALT CEDAR ; SVM METHOD ; LIDAR DATA ; DELINEATION ; IMAGERY ; BIODIVERSITY ; INFORMATION ; DENSITY ; RIVER
资助项目Tianshan Talent-Science and Technology Innovation Team
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001214578200010
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Tianshan Talent-Science and Technology Innovation Team
源URL[http://ir.igsnrr.ac.cn/handle/311030/204839]  
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Li, Junli; Shen, Zhanfeng
作者单位1.Aerosp Informat Res Inst, Chinese Acad Sci, Natl Engn Res Ctr Geomat, Beijing 100101, Peoples R China
2.Inst Geog Sci & Nat Resources Res, Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Sino Belgian Joint Lab Geoinformat, B-9000 Ghent, Belgium
4.Key Lab GIS & RS Applicat Xinjiang Uygur Autonomou, Urumqi 830011, Peoples R China
5.Univ Ghent, Dept Geog, B-9000 Ghent, Belgium
6.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
7.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Sino Belgian Joint Lab Geoinformat, Urumqi 830011, Peoples R China
8.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Haoyu,Li, Junli,Van de Voorde, Tim,et al. Individual Populus euphratica Tree Detection in Sparse Desert Forests Based on Constrained 2-D Bin Packing[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:19.
APA Wang, Haoyu.,Li, Junli.,Van de Voorde, Tim.,Zhou, Chenghu.,De Maeyer, Philippe.,...&Shen, Zhanfeng.(2024).Individual Populus euphratica Tree Detection in Sparse Desert Forests Based on Constrained 2-D Bin Packing.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,19.
MLA Wang, Haoyu,et al."Individual Populus euphratica Tree Detection in Sparse Desert Forests Based on Constrained 2-D Bin Packing".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):19.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。