高空间分辨率遥感影像单株立木识别与树冠分割算法研究
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摘要
树冠参数对于林木生长模型建立、森林经营管理、生态学和木材学研究有重要作用。高空间分辨率遥感单株立木识别和树冠勾绘技术将给森林资源调查带来技术变革,因为它可以提供一致的、可重复客观有效率的测树结果。论文以此选题,从如下几个方面开展研究工作:
1.回顾了从高空间分辨率遥感数据中提取单株立木及林分参数的国内外技术研究现状,特别对遥感图像分割算法和单株识别与树冠勾绘算法,从各种方法的理论模型、信息提取的精度、方法的好用性与适用范围、多种林分条件的实验、不同影像质量数据的实验、真实数据的获取和精度验证方法、方法缺陷的分析以及各种方法的比较实验等角度作了详细地分析。
2.在面向对象影像分析思想和工具的指导下,在树冠模型研究与特征选择的基础上,发展并提出一个基于种子点区域增长的树冠识别和勾绘算法,算法把树冠分割和分类,树梢识别和单株树冠范围的勾绘放在一个统一框架下考虑,能够把相连的树冠分割开来,针对较高郁闭度林分有比较好的效果。算法具体实现步骤如下:首先进行影像的初步分割得到一系列面积较小的影像对象基元,初步分割的目的是在后面的步骤中通过计算影像对象基元的特征选取出杨树树冠的种子点,因此分割尺度值要选得比较小,本文选3。然后缩小树梢检测的范围,选择NDVI特征做掩模运算,把非林分区域屏蔽掉。这样可以开始进行检测树梢,采用非极大值压抑方法,选择近红外像素比率为局部最大值的对象作为杨树树梢种子点。对种子点进行生长,还要去除伪种子点,接着进一步对树冠形状进行优化,使得到的树冠边界比较光滑。最后进行分类合并,树冠属性矢量化及输出。
通过对山西薛家庄林场30小班杨树人工同龄林影像的分割实验研究,得到树冠分布图。研究了两类精度评估技术,即林分整体或平均的精度评估和识别出的立木与实际单株立木一一匹配下的精度评估。通过对9个样地的验证,平均株数满足auto= 1.0892 manual+0.3558,R 2达到0.4693。通过对3个样地的树冠直径进行验证,发现程序计算得到的树冠直径分布规律和地面调查的频数分布有着大致相同的趋势,只是程序计算得到的树冠直径分布的范围更大,并且树冠直径值偏大。通过对不同郁闭度样地的分组分析,发现该算法对0.6郁闭度的林分效果最好,对郁闭度0.8的林分,漏分误差最严重,达34%;对郁闭度0.7的林分误授误差最严重,达63%。研究表明该算法是有效的,在应用时,特征选择、参数值的选择有着重要作用。
3.提出一个基于数学形态学增强运算的标记分水岭分割算法,算法的基本实现步骤如下:读入彩色影像并转换成灰度图像,影像的暗区域是要分割的树冠对象,首先计算分割函数,使用梯度作为分割函数,采用拉普拉斯-高斯算子(LOG)进行边界检测;然后计算前景树梢对象标记,之前先要进行数学形态学的重建开运算和重建闭运算,起到滤波增强的作用,这样每个树冠对象内部都有互相连接的斑点像素;标记是图像特征,有着常量反射率的连接像素,有着一样的纹理的区域,前景标记即树梢。接着计算背景标记,背景标记是树冠或植被最终的范围,对树冠对象识别很有用,采用的是数学形态学的距离变换。这时进行树梢指导下的树冠勾绘,修改分割函数,使它的极小值点在前景和背景标记的位置;最后计算修改后分割函数的分水岭变换。
把该算法用于安徽杨树人工同龄林影像分割实验研究,证明了算法的有效性。该算法对整幅影像分割,不做分类,通过对1个样地影像的验证发现平均株数误差在23%左右。把分水岭影像分割算法用于不同郁闭度的针叶树人工次生林,并以山东徂徕山林场3个样地为例进行验证,验证方法是目视判断,发现在较高郁闭度的林分中该算法的效果要更好些。本算法在实用中后续需要有人为干预。
4.不同于在中低分辨率遥感影像上郁闭度估测常用的回归模型方法,本文应用面向对象影像处理方法提出一种直接计算郁闭度的方法,并计算了山东徂徕山林场针叶树人工次生林航空影像的郁闭度,精度在多数情况下可以保证。本文的方法简单易用。本文在分析了面向对象多尺度影像分析技术中对于树冠信息提取很重要的最合适影像空间分辨率选择的问题。对于以单株识别为目标的研究,首先要解决的问题是:要提取单株,遥感影像合适的空间分辨率是多少。根据前人的经验研究和模拟研究,选择平均树冠直径与像素大小比率或者树冠直径范围与像素大小比率,可以很好地说明树冠识别和轮廓勾绘算法对一些很高空间分辨率影像进行树冠分割得不到预期结果的原因。
5.论文在算法研究的基础上,研究了两种软件工具化方法,即基于面向对象的组件化软件化方法和基于Web服务的方法。尽管在实际的多样林分中应用高空间分辨率光学影像进行树冠识别和轮廓勾绘还有许多技术困难,但是本文获得了有意义的成果,对这一问题有了比较多的知识和技术方法准备,对今后进一步研究有了一些思路。本文的研究提示我们应该基于高空间分辨率遥感影像和Lidar、高光谱等新型传感器构建中国新型森林资源调查体系。通过高空间分辨率遥感影像自动解译估计单株水平的测树属性结合自动解译估计林分因子,希望可以为常规的森林二类调查带来新的技术途径。利用高分数据做专题性的实验和比较,以期来评估基于高分数据的调查能否满足森林调查的精度要求。即使在某些因子不能满足的条件下,新方法提供的新信息,或许在快速一致性或某类专题调查方面具有一定的优势。
To date,the increasing volume and readily availability of high spatial resolution imagery provides an effective source to extract the individual tree information for detailed knowledge of forest stand at different spatial scales. Nevertheless,conventional pixel-based classification methods are far from the use of improved spatial resolution satisfactorily. Recent years ,object based image analysis method with various image engineering techniques becomes a strong support for high spatial resolution imagery object recognition. Automated or semi automated tree detection and crown delineation using high spatial resolution(pixel size is higher than 100 cm) remotely sensed imagery provides a potentially efficient means to acquire information needed for forest management decisions ,sustainable forest management ,mapping damage due to insects and disease and ecology research. Tree detection can provide estimates of tree abundance and spatial pattern that are useful for evaluating density and stocking objectives. Delineation of individual tree crowns can get crown diameter of tree to be used to model tree structural variables such as height,volume,or biomass. Conventional field based plot survey methods which using statistically based plot sampling designs use expert knowledge,but its procedure is highly subjective and highly labour intensive and costly. So automated tree detection and crown delineation with high spatial resolution imagery has the potential to provide the required information in a more objective manner,at lower cost,and with greater coverage than is attainable using field sampling.
Image based automated tree detection and crown delineation algorithm has almost 15 year's research history in north America,north Europe,Germany and Australia. This thesis reviews the main remote sensing segmentation methods and diverse individual tree identification and crown delineation algorithms. We analyze the theoretic model,applicability,precision,experiment condition,verification method,error analyse and limitation of these methods. This thesis focuses on the development of two methods for individual tree identification and crown segmentation in the object based image analysis framework .
The presented approach develops a tree top seeded based region growth tree detection and crown delineation algorithm for analysing QuickBird satellite images in Populus×xiaohei plantation even stand at Xue JiaZhuang wood farm in Shanxi Province of China. This algorithm use the crown model which is focus on basic radiometric properties of tree crowns. We develop a method in which vegetation classification and crown segmentation are derived under a unified framework. After multiresolution segmentation,we get image object segments for tree top seeds detection with NDVI and ratio NIR feature. Around theses seeds,we let them region growing in a cycle way. Some false seeds must be wiped off with given feature threshold. After quadtree segmentation for crown shape optimization,the same category region must be merged. Now we get a crown map of test area. We use 9 plots with different plantation density(crown closure) to validate the above method. Average tree numbers identification error is 18.9% ,R 2 = 0.4693.From comparing tree numbers of field work and software identification by tree matching ,the confusion matrix,overall accuracy,commission error,omission error is computed. The main result is this algorithm’s accuracy,commission error,omission error far from crown closure. Computed crown diameters after program crown delineation has similar distribution of field measure crown diameters,but they have bigger values and more dispersed range. Through grouped plantation density results analyzing,we find the performance of this algorithm on 0.6 crown closure plots get well,omission error of 0.8 crown closure plots is high to 34%,commission error of 0.7 crown closure plots is high to 63%. Ultimately,our tree top seeded based region growth tree detection and crown delineation algorithm is an effective way to get segmented crown in real stand image. We suggest users choose suited features and parameter values try by try in forehand applying.
The presented approach develops a new mathematical morphology based marker-controlled watershed crown segmentation algorithm for crown segmentation. This method is be put on the QuickBird satellite images in Populus I-72 plantation even stand at Nan Gen village Hai Kou town in Anhui Province of China. Segmentation using the watershed transform works better if you can identify or mark foreground objects and background locations. Our marker-controlled watershed crown segmentation algorithm follows basic procedure. First,a segmentation function is computed which put image’s dark regions as the objects you are trying to segment and LOG edge detection is applied. Then,computing foreground treetop markers after morphological reconstruction by opening and closing for filtering. The connected blobs of pixels within each of the crown like objects are treated as foreground treetop markers. Third step is computing background markers for identifying crown area. The boundary of isolated crown or grouped crowns can be got after mathematical morphology distance transformation. Forth step is modifying the segmentation function so that it only has minima at the foreground tree top and background crown boundary marker locations. Fifth step is computing the watershed transform of the modified segmentation function. This algorithm does not take into account the classification and only gets the image segment for further analyzing. We overlap the segmentation result with original image by manually crown delineation. By visual appraise,this algorithm works well. Average tree numbers identification error is 32%.We discuss the improvement ways to get better results.
In the object based image analysis framework,we discuss the best fit spatial resolution choosing for crown segmentation. We use the ratio of average crown diameter to pixel size for experience index weighing the fitting from literature. This ratio can explain some crown segmentation phenomena,such as why in some more fine spatial resolution image,the crown segmentation result is not good as expectation. We extend our mathematical morphology based marker-controlled watershed crown segmentation algorithm to aerial images with 0.25m spatial resolution in secondary planted conifer stand at Cu Lai mountain in Shan Dong Province of China. We overlap the segmentation result with original image by only simple visual appraising. The result is more crowns identified than the field work. The tree top foreground marker technique for this kind of stand must be improved. Using object based image analysis method,after crown area,shadow area and bare soil area classified,we put up a simple formula to computing the crown closure of the sample images. Average computer crown closure calculating precision is high to 80%.
In the end of this thesis,conclusion and discussion is given. Two kinds of program methods for crown segmentation algorithms are put up. One method is using Component Object Model(COM) and the other one is using web service technology. We draw the outline of estimation tree level attributes in automatic interpretation method researched before and put it to automatic interpretation method of estimation of stand level attributes. For all,many aspects of raised tree detection and crown delineation algorithms must be improved for method’s practicality,our research has contribution to the knowledge of this area. Some suggestions are in the end of this paper for tree detection and crown delineation by experience.The crown closure of forest stand and the choosen of best fit spatial resoulution image are the very important factors affect the result of the algorithms.
1.回顾了从高空间分辨率遥感数据中提取单株立木及林分参数的国内外技术研究现状,特别对遥感图像分割算法和单株识别与树冠勾绘算法,从各种方法的理论模型、信息提取的精度、方法的好用性与适用范围、多种林分条件的实验、不同影像质量数据的实验、真实数据的获取和精度验证方法、方法缺陷的分析以及各种方法的比较实验等角度作了详细地分析。
2.在面向对象影像分析思想和工具的指导下,在树冠模型研究与特征选择的基础上,发展并提出一个基于种子点区域增长的树冠识别和勾绘算法,算法把树冠分割和分类,树梢识别和单株树冠范围的勾绘放在一个统一框架下考虑,能够把相连的树冠分割开来,针对较高郁闭度林分有比较好的效果。算法具体实现步骤如下:首先进行影像的初步分割得到一系列面积较小的影像对象基元,初步分割的目的是在后面的步骤中通过计算影像对象基元的特征选取出杨树树冠的种子点,因此分割尺度值要选得比较小,本文选3。然后缩小树梢检测的范围,选择NDVI特征做掩模运算,把非林分区域屏蔽掉。这样可以开始进行检测树梢,采用非极大值压抑方法,选择近红外像素比率为局部最大值的对象作为杨树树梢种子点。对种子点进行生长,还要去除伪种子点,接着进一步对树冠形状进行优化,使得到的树冠边界比较光滑。最后进行分类合并,树冠属性矢量化及输出。
通过对山西薛家庄林场30小班杨树人工同龄林影像的分割实验研究,得到树冠分布图。研究了两类精度评估技术,即林分整体或平均的精度评估和识别出的立木与实际单株立木一一匹配下的精度评估。通过对9个样地的验证,平均株数满足auto= 1.0892 manual+0.3558,R 2达到0.4693。通过对3个样地的树冠直径进行验证,发现程序计算得到的树冠直径分布规律和地面调查的频数分布有着大致相同的趋势,只是程序计算得到的树冠直径分布的范围更大,并且树冠直径值偏大。通过对不同郁闭度样地的分组分析,发现该算法对0.6郁闭度的林分效果最好,对郁闭度0.8的林分,漏分误差最严重,达34%;对郁闭度0.7的林分误授误差最严重,达63%。研究表明该算法是有效的,在应用时,特征选择、参数值的选择有着重要作用。
3.提出一个基于数学形态学增强运算的标记分水岭分割算法,算法的基本实现步骤如下:读入彩色影像并转换成灰度图像,影像的暗区域是要分割的树冠对象,首先计算分割函数,使用梯度作为分割函数,采用拉普拉斯-高斯算子(LOG)进行边界检测;然后计算前景树梢对象标记,之前先要进行数学形态学的重建开运算和重建闭运算,起到滤波增强的作用,这样每个树冠对象内部都有互相连接的斑点像素;标记是图像特征,有着常量反射率的连接像素,有着一样的纹理的区域,前景标记即树梢。接着计算背景标记,背景标记是树冠或植被最终的范围,对树冠对象识别很有用,采用的是数学形态学的距离变换。这时进行树梢指导下的树冠勾绘,修改分割函数,使它的极小值点在前景和背景标记的位置;最后计算修改后分割函数的分水岭变换。
把该算法用于安徽杨树人工同龄林影像分割实验研究,证明了算法的有效性。该算法对整幅影像分割,不做分类,通过对1个样地影像的验证发现平均株数误差在23%左右。把分水岭影像分割算法用于不同郁闭度的针叶树人工次生林,并以山东徂徕山林场3个样地为例进行验证,验证方法是目视判断,发现在较高郁闭度的林分中该算法的效果要更好些。本算法在实用中后续需要有人为干预。
4.不同于在中低分辨率遥感影像上郁闭度估测常用的回归模型方法,本文应用面向对象影像处理方法提出一种直接计算郁闭度的方法,并计算了山东徂徕山林场针叶树人工次生林航空影像的郁闭度,精度在多数情况下可以保证。本文的方法简单易用。本文在分析了面向对象多尺度影像分析技术中对于树冠信息提取很重要的最合适影像空间分辨率选择的问题。对于以单株识别为目标的研究,首先要解决的问题是:要提取单株,遥感影像合适的空间分辨率是多少。根据前人的经验研究和模拟研究,选择平均树冠直径与像素大小比率或者树冠直径范围与像素大小比率,可以很好地说明树冠识别和轮廓勾绘算法对一些很高空间分辨率影像进行树冠分割得不到预期结果的原因。
5.论文在算法研究的基础上,研究了两种软件工具化方法,即基于面向对象的组件化软件化方法和基于Web服务的方法。尽管在实际的多样林分中应用高空间分辨率光学影像进行树冠识别和轮廓勾绘还有许多技术困难,但是本文获得了有意义的成果,对这一问题有了比较多的知识和技术方法准备,对今后进一步研究有了一些思路。本文的研究提示我们应该基于高空间分辨率遥感影像和Lidar、高光谱等新型传感器构建中国新型森林资源调查体系。通过高空间分辨率遥感影像自动解译估计单株水平的测树属性结合自动解译估计林分因子,希望可以为常规的森林二类调查带来新的技术途径。利用高分数据做专题性的实验和比较,以期来评估基于高分数据的调查能否满足森林调查的精度要求。即使在某些因子不能满足的条件下,新方法提供的新信息,或许在快速一致性或某类专题调查方面具有一定的优势。
To date,the increasing volume and readily availability of high spatial resolution imagery provides an effective source to extract the individual tree information for detailed knowledge of forest stand at different spatial scales. Nevertheless,conventional pixel-based classification methods are far from the use of improved spatial resolution satisfactorily. Recent years ,object based image analysis method with various image engineering techniques becomes a strong support for high spatial resolution imagery object recognition. Automated or semi automated tree detection and crown delineation using high spatial resolution(pixel size is higher than 100 cm) remotely sensed imagery provides a potentially efficient means to acquire information needed for forest management decisions ,sustainable forest management ,mapping damage due to insects and disease and ecology research. Tree detection can provide estimates of tree abundance and spatial pattern that are useful for evaluating density and stocking objectives. Delineation of individual tree crowns can get crown diameter of tree to be used to model tree structural variables such as height,volume,or biomass. Conventional field based plot survey methods which using statistically based plot sampling designs use expert knowledge,but its procedure is highly subjective and highly labour intensive and costly. So automated tree detection and crown delineation with high spatial resolution imagery has the potential to provide the required information in a more objective manner,at lower cost,and with greater coverage than is attainable using field sampling.
Image based automated tree detection and crown delineation algorithm has almost 15 year's research history in north America,north Europe,Germany and Australia. This thesis reviews the main remote sensing segmentation methods and diverse individual tree identification and crown delineation algorithms. We analyze the theoretic model,applicability,precision,experiment condition,verification method,error analyse and limitation of these methods. This thesis focuses on the development of two methods for individual tree identification and crown segmentation in the object based image analysis framework .
The presented approach develops a tree top seeded based region growth tree detection and crown delineation algorithm for analysing QuickBird satellite images in Populus×xiaohei plantation even stand at Xue JiaZhuang wood farm in Shanxi Province of China. This algorithm use the crown model which is focus on basic radiometric properties of tree crowns. We develop a method in which vegetation classification and crown segmentation are derived under a unified framework. After multiresolution segmentation,we get image object segments for tree top seeds detection with NDVI and ratio NIR feature. Around theses seeds,we let them region growing in a cycle way. Some false seeds must be wiped off with given feature threshold. After quadtree segmentation for crown shape optimization,the same category region must be merged. Now we get a crown map of test area. We use 9 plots with different plantation density(crown closure) to validate the above method. Average tree numbers identification error is 18.9% ,R 2 = 0.4693.From comparing tree numbers of field work and software identification by tree matching ,the confusion matrix,overall accuracy,commission error,omission error is computed. The main result is this algorithm’s accuracy,commission error,omission error far from crown closure. Computed crown diameters after program crown delineation has similar distribution of field measure crown diameters,but they have bigger values and more dispersed range. Through grouped plantation density results analyzing,we find the performance of this algorithm on 0.6 crown closure plots get well,omission error of 0.8 crown closure plots is high to 34%,commission error of 0.7 crown closure plots is high to 63%. Ultimately,our tree top seeded based region growth tree detection and crown delineation algorithm is an effective way to get segmented crown in real stand image. We suggest users choose suited features and parameter values try by try in forehand applying.
The presented approach develops a new mathematical morphology based marker-controlled watershed crown segmentation algorithm for crown segmentation. This method is be put on the QuickBird satellite images in Populus I-72 plantation even stand at Nan Gen village Hai Kou town in Anhui Province of China. Segmentation using the watershed transform works better if you can identify or mark foreground objects and background locations. Our marker-controlled watershed crown segmentation algorithm follows basic procedure. First,a segmentation function is computed which put image’s dark regions as the objects you are trying to segment and LOG edge detection is applied. Then,computing foreground treetop markers after morphological reconstruction by opening and closing for filtering. The connected blobs of pixels within each of the crown like objects are treated as foreground treetop markers. Third step is computing background markers for identifying crown area. The boundary of isolated crown or grouped crowns can be got after mathematical morphology distance transformation. Forth step is modifying the segmentation function so that it only has minima at the foreground tree top and background crown boundary marker locations. Fifth step is computing the watershed transform of the modified segmentation function. This algorithm does not take into account the classification and only gets the image segment for further analyzing. We overlap the segmentation result with original image by manually crown delineation. By visual appraise,this algorithm works well. Average tree numbers identification error is 32%.We discuss the improvement ways to get better results.
In the object based image analysis framework,we discuss the best fit spatial resolution choosing for crown segmentation. We use the ratio of average crown diameter to pixel size for experience index weighing the fitting from literature. This ratio can explain some crown segmentation phenomena,such as why in some more fine spatial resolution image,the crown segmentation result is not good as expectation. We extend our mathematical morphology based marker-controlled watershed crown segmentation algorithm to aerial images with 0.25m spatial resolution in secondary planted conifer stand at Cu Lai mountain in Shan Dong Province of China. We overlap the segmentation result with original image by only simple visual appraising. The result is more crowns identified than the field work. The tree top foreground marker technique for this kind of stand must be improved. Using object based image analysis method,after crown area,shadow area and bare soil area classified,we put up a simple formula to computing the crown closure of the sample images. Average computer crown closure calculating precision is high to 80%.
In the end of this thesis,conclusion and discussion is given. Two kinds of program methods for crown segmentation algorithms are put up. One method is using Component Object Model(COM) and the other one is using web service technology. We draw the outline of estimation tree level attributes in automatic interpretation method researched before and put it to automatic interpretation method of estimation of stand level attributes. For all,many aspects of raised tree detection and crown delineation algorithms must be improved for method’s practicality,our research has contribution to the knowledge of this area. Some suggestions are in the end of this paper for tree detection and crown delineation by experience.The crown closure of forest stand and the choosen of best fit spatial resoulution image are the very important factors affect the result of the algorithms.
引文
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