Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 206-212.doi: 10.11896/JsJkx.191100138

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Remote Sensing Image Single Tree Detection Based on Active Contour Evolution Model

YE Yang, ZHOU Qi-zheng, SHEN Ying and FAN Jing   

  1. ZheJiang Univerisity of Technology,Hangzhou 310012,China
  • Published:2020-07-07
  • About author:YE Yang, born in 1980, postgraduate, lab master, is a member of China Computer Federation.His main research interests include virtual reality, digital image processing.
    FAN Jing, born in 1969, Ph.D, professor, is a member of China Computer Federation.Her main research direction include virtual reality, service.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61572437),2018 Innovative Experiment ProJect (PX-68182044) and Education Department ProJect(Y201431824).

Abstract: Single-wood detection is a method of automatically or semi-automatically acquiring single tree information by combining remote sensing imagery with computer vision technology.Aiming at the phenomenon that a large number of trees cover each other in complex forest scenes,and the excessive extraction of crown vertices and the outline of crown caused by a large number of weak edges inside the crown,a remote tree image detection method based on active contour evolution model is proposed.The method divides the shadow control area based on the prior knowledge of the positive correlation between the number of shades and the number of trees,and uses the shape centroid as the crown apex.Then the morphological active contour evolution model (Snake model) optimized by the illumination angle is used to describe the crowncontour,so that it can cross the weak boundary point;finally optimize the crown profile according to the shape feature.The experimental results show that the method improves the accuracy of single tree wood information extraction in complex forest scenes,reduces the misrecognition rate of crown extraction process,and makes the crown contour shape more accurate.

Key words: Active contour evolution, Complex forest scene, Crown profile, Shadow control method, Single wood detection

CLC Number: 

  • TP39
[1] LIU X X,HUANG J W,YAN H B.Automatic extraction method and application of single-wood canopy for high spatial resolution remote sensing.Journal of ZheJiang A and F University,2010,27(1):126-133.
[2] 2POLLOCK R.The automatic recognition of individual trees in aerial images of forests based on a synthetic tree crown image model.University of British Columbia,1996.
[3] LARSEN M,RUDEMO M.Optimizing templates for findingtrees in aerial photographs.Pattern Recognition Letters,1998,19(12):1153-1162.
[4] WARNER T A,LEE J Y,MCGRAW J B.Delineation and identification of individual trees in the eastern deciduous forest.Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry,1998:10-12.
[5] TARP-JOHANSEN M J.Automatic stem mapping in three dimensions by template matching from aerial photographs.Scandinavian Journal of Forest Research,2002,17(4):359-368.
[6] WANG L,GONG P,BIGING G S.Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery.Photogrammetric Engineering & Remote Sensing,2004,70(3):351-357.
[7] TOCHON G,FRET J B,VALERO S,et al.On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images.Remote Sensing of Environment,2015,159:318-331.
[8] [8]SAMBUGARO M,COLPI C,MARZANO R,et al.Utilizzo del telerilevamento per l’analisidellabiodiversitàstrutturale:ilcaso studio dellaRiservaForestale di Clise (Asiago,VI)//Proceedings of the 17th ConferenzaNazionale ASITA.Riva del Garda,Italy,2013:5-7.
[9] CULVENOR D S.TIDA:an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery.Computers & Geosciences,2002,28(1):33-44.
[10] ERIKSON M.Two preprocessing techniques based on grey level and geometric thickness to improve segmentation results.Pattern Recognition Letters,2006,27(3):160-166.
[11] ZHEN Z,QUACKENBUSH L J,ZHANG L.Trends in automatic individual tree crown detection and delineation-evolution of lidardata.Remote Sensing,2016,8(4):333.
[12] KASS M,WITKIN A,TERZOPOULOS D.Snakes:Active contour models.International Journal of Computer Vision,1988,1(4):321-331.
[13] COHEN L D,COHEN I.A finite element method applied tonew active contour models and 3D reconstruction from cross sections//International Conference on Computer Vision.IEEE,1990:587-591.
[14] JUMAAT A K,RAHMAN W E Z W A,IBRAHIM A,et al.Segmentation of masses from breast ultrasound images using parametric active contour algorithm.Procedia-Social and Behavioral Sciences,2010,8:640-647.
[15] [15]KABOLIZADE M,EBADI H,AHMADI S.An improved snake model for automatic extraction of buildings from urban aerial images and LiDAR data.Computers Environment & Urban Systems,2010,34(5):435-441.
[1] CHEN Zhi-qiang, HAN Meng, LI Mu-hang, WU Hong-xin, ZHANG Xi-long. Survey of Concept Drift Handling Methods in Data Streams [J]. Computer Science, 2022, 49(9): 14-32.
[2] WANG Ming, WU Wen-fang, WANG Da-ling, FENG Shi, ZHANG Yi-fei. Generative Link Tree:A Counterfactual Explanation Generation Approach with High Data Fidelity [J]. Computer Science, 2022, 49(9): 33-40.
[3] ZHANG Jia, DONG Shou-bin. Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer [J]. Computer Science, 2022, 49(9): 41-47.
[4] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[5] SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69.
[6] CHAI Hui-min, ZHANG Yong, FANG Min. Aerial Target Grouping Method Based on Feature Similarity Clustering [J]. Computer Science, 2022, 49(9): 70-75.
[7] ZHENG Wen-ping, LIU Mei-lin, YANG Gui. Community Detection Algorithm Based on Node Stability and Neighbor Similarity [J]. Computer Science, 2022, 49(9): 83-91.
[8] LYU Xiao-feng, ZHAO Shu-liang, GAO Heng-da, WU Yong-liang, ZHANG Bao-qi. Short Texts Feautre Enrichment Method Based on Heterogeneous Information Network [J]. Computer Science, 2022, 49(9): 92-100.
[9] XU Tian-hui, GUO Qiang, ZHANG Cai-ming. Time Series Data Anomaly Detection Based on Total Variation Ratio Separation Distance [J]. Computer Science, 2022, 49(9): 101-110.
[10] NIE Xiu-shan, PAN Jia-nan, TAN Zhi-fang, LIU Xin-fang, GUO Jie, YIN Yi-long. Overview of Natural Language Video Localization [J]. Computer Science, 2022, 49(9): 111-122.
[11] CAO Xiao-wen, LIANG Mei-yu, LU Kang-kang. Fine-grained Semantic Reasoning Based Cross-media Dual-way Adversarial Hashing Learning Model [J]. Computer Science, 2022, 49(9): 123-131.
[12] ZHOU Xu, QIAN Sheng-sheng, LI Zhang-ming, FANG Quan, XU Chang-sheng. Dual Variational Multi-modal Attention Network for Incomplete Social Event Classification [J]. Computer Science, 2022, 49(9): 132-138.
[13] DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145.
[14] QU Qian-wen, CHE Xiao-ping, QU Chen-xin, LI Jin-ru. Study on Information Perception Based User Presence in Virtual Reality [J]. Computer Science, 2022, 49(9): 146-154.
[15] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!