基于标记控制分水岭分割方法的高分辨率遥感影像单木树冠提取

doi:10.3724/SP.J.1047.2016.01259

摘要/Abstract

摘要：

树冠是树木的重要组成,可以直接反映树木健康状况。高空间分辨率遥感影像和遥感技术为快速获取详细的树冠信息和实时监测林冠变化提供了有效的途径。因此,基于高空间分辨率遥感影像的单木树冠提取方法研究对现代森林管理具有重要意义。本文以黄河三角洲地区孤岛林场人工刺槐林和旱柳为研究对象,以QuickBird影像为数据源,首先利用面向对象方法实现研究区林地和非林地分类;然后以林地为掩膜,提取出树冠分布范围;在此基础上,分别选取疏林区和密林区为试验区域,通过形态学开闭重建滤波,平滑图像,去除噪声;最后,利用标记控制分水岭分割方法分别对疏林区和密林区进行树冠提取。本文以人工勾绘结果为参考进行精度验证,结果显示疏林区F测度达到87.8%,密林区F测度达到65.5%,表明该提取方法简单易行,精度可靠。

关键词: QuickBird, 树冠提取, 面向对象分类, 标记控制分水岭

Abstract:

Since the 1990s, a large number of trees in the Gudao forest area have been threatened by both the natural and human effects, which caused huge losses to the forestry development of the Yellow River Delta. Therefore, the more detailed and timely information of forests is required for improving the local forest management and ecosystem monitoring. Individual tree crown delineation has been demonstrated to be an efficient way to carry out forestry inventories at a fine scale, as the tree crown serves as the basic unit for biomass measurement, species recognition, or health monitoring, etc. The increasing availability and affordability of high spatial resolution remote sensing images provides great potential for accurately characterizing the tree crown. Current tree crown extraction methods based on the imagery were easily interfered by the background objects, especially when there were mixtures of trees, shrubs and grass. A conventional watershed segmentation approach could cause over-segmentation of the imagery because the branches in the imagery are similar to some small tree crowns in shape and scale. To overcome such issues, a new tree crown extraction method based on the object-oriented extraction and the marker-controlled watershed segmentation with the morphological filtering was presented in this paper to extract the individual tree crown from high spatial resolution image. Firstly, the tree crown area was separated from the background using the object-oriented method. After that, the morphological opening and closing operators were employed to smooth the image and remove the noises from the image. Then, we located the tree tops by detecting the local maximum. These tree tops were recognized as markers for the subsequent segmentation. Finally, the tree crowns were delineated through the marker-controlled watershed method. Based on the QuickBird image, we applied this method to the deciduous tree crown delineation of Gudao forest area in the Yellow River Delta. Both the sparse forest area and the dense forest area are tested in order to verify the universality and applicability. To evaluate the method proposed in this paper, we conducted a visual interpretation based on the original images of two plots, and the resulted manually delineated crown maps were then regarded as the reference maps. The delineation accuracies were assessed by comparing the spatial relationships of the automated delineated crowns and the reference crowns. The results showed that the overall extraction accuracy is 87.8% for the sparse forest area, and 65.5% for the dense forest area, which indicated that the tree crown extraction method proposed in this study is effective and promising.

Key words: QuickBird, the extraction of tree crown, object-oriented classification, marker-controlled watershed

郭昱杉, 刘庆生, 刘高焕, 黄翀. 基于标记控制分水岭分割方法的高分辨率遥感影像单木树冠提取[J]. 地球信息科学学报, 2016, 18(9): 1259-1266.DOI:10.3724/SP.J.1047.2016.01259

GUO Yushan,LIU Qingsheng,LIU Gaohuan,HUANG Chong. Individual Tree Crown Extraction of High Resolution Image Based on Marker-controlled Watershed Segmentation Method[J]. Journal of Geo-information Science, 2016, 18(9): 1259-1266.DOI:10.3724/SP.J.1047.2016.01259

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参考文献 23

[1]	Pouliot D A, King D J, Bell F W, et al.Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration[J]. Remote Sensing of Environment, 2002,82(2):322-334. doi: 10.1016/S0034-4257(02)00050-0
[2]	Chen Q, Baldocchi D, Gong P, Isolating individual trees in a savanna woodland using small footprint lidar data[J]. Photogrammetric Engineering & Remote Sensing, 2006,72(8):923-932. doi: 10.14358/PERS.72.8.923
[3]	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. doi: 10.14358/PERS.70.3.351
[4]	Ke Y, Quackenbush L J.Individual tree crown detection and delineation from high spatial resolution imagery using active contour and hill-climbing methods[C]. Proceedings of 2009 ASPRS Annual Conference, American Society of Photogrammetry and Remote Sensing, 2009:9-13.
[5]	Gougeon F A.A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images[J]. Canadian Journal of Remote Sensing, 1995,21(3):274-284.
[6]	Pollock R J.The automatic recognition of individual trees in aerial images of forests based on a synthetic tree crown image model[D]. Concordia University, 1996.
[7]	Brandtberg T, Walter F.Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis[J]. Machine Vision and Applications, 1998,11(2):64-73. doi: 10.1007/s001380050091
[8]	Jing L, Hu B, Noland T, et al.An individual tree crown delineation method based on multi-scale segmentation of imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012,70:88-98. doi: 10.1016/j.isprsjprs.2012.04.003
[9]	覃先林,李增元,易浩若.高空间分辨率卫星遥感影像树冠信息提取方法研究[J].遥感技术与应用,2005(2):228-232. doi: 10.3969/j.issn.1004-0323.2005.02.003
	[ Qin X L, Li Z Y, Yi H R.Extraction method of tree crown using high-resolution satellite Image[J]. Remote Sensing Technology & Application, 2005,2:228-232. ] doi: 10.3969/j.issn.1004-0323.2005.02.003
[10]	Liu Q S, Liu G H, Yao L.Changes of Robinia Pseudoacacia planted forest in the Yiqianer nature reserve following the super storm surge of sept. 1992[C]. The 3rd International Yellow River Forum on Sustainable Water Resources Management and Delta Ecosystem Maintenance, 2007:116-120.
[11]	姚玲,刘高焕,刘庆生,等.利用影像分类分析黄河三角洲人工刺槐林健康[J].武汉大学学报·信息科学版,2010,35(7):863-867.
	[ Yao L, Liu G H, Liu Q S, et al.Remote sensing monitoring the health of artificial Robinia pseudoacacia forest[J]. Geomatics & Information Science of Wuhan University, 2010,35(7):863-867. ]
[12]	刘庆生,刘高焕,姚玲.利用Landsat ETM+数据检测人工刺槐林冠健康[J].遥感技术与应用,2008(2):142-146,109. doi: 10.11873/j.issn.1004-0323.2008.2.142
	[ Liu Q S, Liu G H, Yao L.Detection of Robinia pseudoacacia planted forest canopy health using Landsat ETM+ image data[J]. Remote Sensing Technology & Application, 2008,2:142-146,109. ] doi: 10.11873/j.issn.1004-0323.2008.2.142
[13]	Wang H, Pu R, Zhu Q, et al.Mapping health levels of Robinia pseudoacacia forests in the Yellow River delta, China, using IKONOS and Landsat 8 OLI imagery[J]. International Journal of Remote Sensing, 2015,36(4):1114-1135. doi: 10.1080/01431161.2015.1007253
[14]	张珍珍,王红.基于卫星IKONOS影像的黄河三角洲人工刺槐林健康状况分类[J].科学技术与工程,2014,(33):73-79. doi: 10.3969/j.issn.1671-1815.2014.33.014
	[ Zhang Z Z, Wang H.Health situation classification of artificial Robinia pseudoacacia forests from Yellow River Delta based on IKONOS[J]. Science Technology & Engineering, 2014,33:73-79. ] doi: 10.3969/j.issn.1671-1815.2014.33.014
[15]	宗秀影,刘高焕,乔玉良,等.黄河三角洲湿地景观格局动态变化分析[J].地球信息科学学报,2009,11(1):91-97.
	[ Zong X Y, Liu G H, Qiao Y L, et al.Study on dynamic changes of wetland landscape pattern in Yellow River Delta[J]. Geo-information Science, 2009,11(1):91-97. ]
[16]	刘庆生,刘高焕,黄翀.黄河三角洲人工刺槐林枯梢调查统计分析[J].林业资源管理,2011(5):79-83. doi: 10.3969/j.issn.1002-6622.2011.05.015
	[ Liu Q S, Liu G H, Huang C.Statistic analysis on planted Robinia pseudoacacia forest dieback in the Yellow River Delta[J]. Forest Resources Management, 2011,5:79-83. ] doi: 10.3969/j.issn.1002-6622.2011.05.015
[17]	张存. 基于Quick Bird影像的面向对象信息提取方法比较实验研究[D].沈阳:东北大学,2009.
	[ Zhang C.Comparative experimentation study on Information extraction of object-oriented based on QuickBird image[D]. Shenyang: Northeastern University, 2009. ]
[18]	张聪颖,田淑芳.基于ENVI Zoom面向对象的高分辨率影像的道路提取[J].中国科技信息,2010(23):96-97. doi: 10.3969/j.issn.1001-8972.2010.23.044
	[ Zhang C Y, Tian S F.Road extraction based on ENVI Zoom from high-resolution image using objected-oriented Technology[J]. China Science and Technology Information, 2010,23:96-97. ] doi: 10.3969/j.issn.1001-8972.2010.23.044
[19]	罗玲,解梅,陈杉.基于多尺度形态滤波的分水岭图像分割方法[J].计算机辅助设计与图形学学报,2004(2):168-173. doi: 10.3321/j.issn:1003-9775.2004.02.004
	[ Luo L, Xie M, Chen S.Watershed segmentation based on multi-scale morphological filtering[J]. Journal of Computer Aided Design & Computer Graphics, 2004,2:168-173. ] doi: 10.3321/j.issn:1003-9775.2004.02.004
[20]	王小鹏,罗进文.基于形态学梯度重建的分水岭分割[J].光电子·激光,2005(1):98-101. doi: 10.3321/j.issn:1005-0086.2005.01.024
	[ Wang X P, Luo J W.Watershed segmentation based on morphological gradient reconstruction[J]. Journal of Optoelectronics Laser, 2005,1:98-101. ] doi: 10.3321/j.issn:1005-0086.2005.01.024
[21]	Vincent L, Soille P.Watersheds in digital spaces: an efficient algorithm based on immersion simulations[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1991(6):583-598. doi: 10.1109/34.87344
[22]	马丽红,张宇,邓健平.基于形态开闭滤波二值标记和纹理特征合并的分水岭算法[J].中国图象图形学报,2003,8(1):80-86. doi: 10.3969/j.issn.1006-8961.2003.01.013
	[ Ma L H, Zhang Y, Deng J P.A target segmentation algorithm based on opening-closing binary marker on watersheds and texture merging[J]. Journal of Image & Graphics, 2003,8(1):80-86. ] doi: 10.3969/j.issn.1006-8961.2003.01.013
[23]	Soille P.Morphological image analysis: principles and applications[M]. Berlin, Germany: Springer Science & Business Media, 2013.