基于形态Snake模型的遥感影像的单木树冠检测算法
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摘要
单木树冠检测可以辅助林业统计获取诸如树冠位置、冠幅、胸径等信息,对发展精准林业具有重大意义。针对单木树冠检测中树冠轮廓描绘不精确的问题,文中提出了一种基于形态Snake模型的遥感影像的单木树冠检测算法。该算法首先对林区特征进行了分析,然后使用局部极值法对林区特征图和距离变换图提取树冠顶点,最后根据树冠顶点为所有树冠初始化形态Snake模型轮廓,并迭代进行轮廓演变,得到最终的树冠轮廓。为了验证方法的有效性,对比分析了区域生长法、模板匹配法、分水岭法和所提出的形态Snake模型法。实验结果表明,所提方法的检测结果更准确,树冠轮廓更接近实际形状,与其他已有方法相比,整体检测得分提高了6%,面积平均差降低了0.5m2。
Single tree detection can assist forestry statistics in getting information such as position,width and diameter of the crowns,so it is of great significance for the development of precision forestry.In order to solve the problem of inaccurate canopy delineation in single-tree canopy detection,this paper proposed a single tree detection algorithm based on morphological Snake model for remote sensing images.Firstly,the forest features are analyzed.Then the local maximum method is used to extract treetops according to the forest feature map and the distance map.After this,the contour of Snake model is initialized for all crowns according to treetops,after evolution of the contour,the final detection result of individual trees is obtained.In order to verify the effectiveness of the method,this paper gave comparative analysis of the region growing method,template matching method,watershed method and the proposed morphological snake model method.The experimental results show that the proposed method is more accurate and the shape of the crown is more realistic.Compared with the other three methods,the detection score is 6% higher and the area average difference is reduced by 0.5 m2.
Single tree detection can assist forestry statistics in getting information such as position,width and diameter of the crowns,so it is of great significance for the development of precision forestry.In order to solve the problem of inaccurate canopy delineation in single-tree canopy detection,this paper proposed a single tree detection algorithm based on morphological Snake model for remote sensing images.Firstly,the forest features are analyzed.Then the local maximum method is used to extract treetops according to the forest feature map and the distance map.After this,the contour of Snake model is initialized for all crowns according to treetops,after evolution of the contour,the final detection result of individual trees is obtained.In order to verify the effectiveness of the method,this paper gave comparative analysis of the region growing method,template matching method,watershed method and the proposed morphological snake model method.The experimental results show that the proposed method is more accurate and the shape of the crown is more realistic.Compared with the other three methods,the detection score is 6% higher and the area average difference is reduced by 0.5 m2.
引文
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[12]汪梅,李琳,汪斌,等.分水岭算法应用于主动轮廓模型能量分割算法的研究[J].计算机科学,2017,44(5):314-319.
[13]LVAREZ L,BAUMELA L,HENRQUEZ P,et al.Morphological snakes[C]∥2010IEEE Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2010:2197-2202.
[14]ZHEN Z,QUACKENBUSH L J,STEHMAN S V,et al.Agentbased region growing for individual tree crown delineation from airborne laser scanning(ALS)data[J].International Journal of Remote Sensing,2015,36(7):1965-1993.
[15]HUNG C,BRYSON M,SUKKARIEH S.Multi-class predictive template for tree crown detection[J].Isprs Journal of Photogrammetry&Remote Sensing,2012,68(3):170-183.
[16]YANG J,HE Y,CASPERSEN J.A multi-band watershed segmentation method for individual tree crown delineation from high resolution multispectral aerial image[C]∥2014IEEE International Geoscience and Remote Sensing Symposium(IGARSS).IEEE,2014:1588-1591.
[2] HYYPP J,YU X,HYYPP H,et al.Advances in Forest Inventory Using Airborne Laser Scanning[J].Remote Sensing,2012,4(5):1190-1207.
[3] PINZ A.A computer vision system for recognition of trees in aerial photographs[C]∥International Association of Pattern Recognition Workshop,NASA Conference Publication 3099.1991.
[4] ERIKSSON M,PERRIN G,DESCOMBES X,et al.A comparative study of three methods for identifying individual tree crowns in aerial images covering different types of forests[C]∥Proceedings of International Society for Photgrammetry&Remote Sensing Marne La.2006.
[5] WANG L,GONG P,BIGING G S.Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery[J].Photogrammetric Engineering&Remote Sensing,2004,70(3):351-357.
[6] TARP-JOHANSEN M J.Automatic stem mapping in three dimensions by template matching from aerial photographs[J].Scandinavian Journal of Forest Research,2002,17(4):359-368.
[7] ZHAO Y,HAO Y,ZHEN Z,et al.A Region-Based Hierarchical Cross-Section Analysis for Individual Tree Crown Delineation Using ALS Data[J].Remote Sensing,2017,9(10):1084.
[8] ZAREMBA M B,GOUGEON F A.Fusion of High-Resolution Satellite and Lidar Data for Individual Tree Recognition[C]∥Proceedings of the Canadian Conference on Electrical and Computer Engineering(CCECE 2006).Ottawa Congress Centre,Ottawa,Canada.2006:1112-1115.
[9] KASS M,WITKIN A,TERZOPOULOS D.Snakes:Active contour models[J].International Journal of Computer Vision,1988,1(4):321-331.
[10]COHEN L D,COHEN I.A finite element method applied to new active contour models and 3Dreconstruction from cross sections[C]∥International Conference on Computer Vision.IEEE,1990:587-591.
[11]XU C Y,PRINCE J L.Gradient vector flow[M]∥Computer Vision.Springer US,2014:349-354.
[12]汪梅,李琳,汪斌,等.分水岭算法应用于主动轮廓模型能量分割算法的研究[J].计算机科学,2017,44(5):314-319.
[13]LVAREZ L,BAUMELA L,HENRQUEZ P,et al.Morphological snakes[C]∥2010IEEE Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2010:2197-2202.
[14]ZHEN Z,QUACKENBUSH L J,STEHMAN S V,et al.Agentbased region growing for individual tree crown delineation from airborne laser scanning(ALS)data[J].International Journal of Remote Sensing,2015,36(7):1965-1993.
[15]HUNG C,BRYSON M,SUKKARIEH S.Multi-class predictive template for tree crown detection[J].Isprs Journal of Photogrammetry&Remote Sensing,2012,68(3):170-183.
[16]YANG J,HE Y,CASPERSEN J.A multi-band watershed segmentation method for individual tree crown delineation from high resolution multispectral aerial image[C]∥2014IEEE International Geoscience and Remote Sensing Symposium(IGARSS).IEEE,2014:1588-1591.