越南大陆海岸线遥感智能解译方法研究
发布时间：2019-02-19 11:57
【摘要】：海岸线是指平均大潮高潮时的水陆分界线,是地形图和海图的基础要素。受自然因素和人为因素的影响,海岸线不断变化,快速而准确地监测海岸线位置和性质的动态变化,对于基础地理信息快速更新、资源调查和海岸带的科学管理具有重要意义。本文旨在研究从遥感影像中快速、准确地获取海岸线信息的方法,在863重大项目课题“南海及其邻域空间情势综合分析与决策模拟”的支持下,以越南大陆海岸线为研究对象,将越南大陆海岸线分为五种类型,使用Landsat8的OLI影像,以影像最优分割、影像特征优选与影像智能分类技术为研究基础,构建多层次的海岸线智能解译模型,采用多种机器学习方法对越南大陆海岸线的智能解译展开了深入研究。论文的主要内容和创新如下:1.针对影像分割中的尺度选择问题,提出了基于知识反馈的最优分割尺度获取方法,根据地物类的尺度判断特征获取尺度备选集,在不同类的地物特征差异最大的理论指导下构建特征差异判别准则,最后利用特征差异判别准则从尺度备选集中获取最优分割尺度。实验表明,该方法能够根据解译任务精确地获取多个目标地物的最优分割尺度。以获取的最优分割尺度为依据,结合聚类方法分析地物类别的可分性,确定了具体的地物类别和信息提取的层次结构。2.系统地分析和评价了基于OLI影像定义的多种特征。基于Pearson样本相关系数评价特征之间的相关性,使用变异系数来衡量特征携带的信息量,使用单因素方差分析的方法分析特征与目标地类之间的相关性,通过关联规则挖掘,得到与10个地物类强相关的特征并对规则进行解译。3.提出了以降低解译不确定性为目标的特征选择方法。基于多种方法对特征进行优选:基于BP神经网络的权值进行敏感性分析,通过敏感性系数的大小对特征的重要性进行排序并进行特征选择;从减少分类不确定性,增强规则表达的鲁棒性等方面考虑,研究了外界因素和内在因素对特征值的影响,外界因素中研究了影像中薄云的影响,内在因素研究了影像分割尺度对影像对象特征值的影响;使用因子分析法对原始的高维特征进行特征抽取实现维度归约。4.对比分析了五种类型常用的机器学习算法在海岸地物分类中的应用效果,通过一系列实验得到各分类器的较优参数设置,构建了适用于海岸地物分类的机器学习模型,并通过影像分类实验分析各个分类器对地物类的分类能力,实验表明,SVM对10个地物类别的分类精度都很高,分类效果最好,SVM和随机森林两种分类器的分类结果互补性强。5.提出了一种使用关系规则进行分类的方法,这种规则通过比较属性或特征间的关系实现分类,具有易于理解和鲁棒的特点。本文首先构造影像对象的关系特征,然后通过机器学习获得了植被相关、水体相关和砂石相关三个大类的多条关系规则,对影像解译的专家知识进行补充。6.基于获取的海岸线数据进行了多种方式的统计分析,得出的主要结论有:2013年越南大陆海岸线总长度约为4067km,其中人工岸线占的比例最大、淤泥质岸线占的比例最小;人工岸线分布最广,砂质岸线主要分布于越南的南中部,基岩岸线分布于越南中部,红树林岸线分布于越南大陆的南北两头,淤泥质海岸在整条大陆海岸线上零星分布;28个沿海省级行政区中海岸线最长的是广宁省,最短的是宁平省,人工岸线最长的是南定省,基岩岸线最长的是庆和省,砂质岸线最长的是平顺省,淤泥质岸线最长的是广宁省,红树林岸线最长的是金瓯省。
[Abstract]:The coastline refers to the surface boundary between the average tide and the tide, which is the basic element of the topographic map and the chart. With the influence of natural and human factors, the change of the coastline, the rapid and accurate monitoring of the dynamic changes of the position and the nature of the coastline is of great significance to the rapid updating of the basic geographic information, the resource investigation and the scientific management of the coastal zone. The purpose of this paper is to study the method of fast and accurate acquisition of the coastline information from the remote sensing image. With the support of the comprehensive analysis and decision-making simulation of the South China Sea and its neighborhood space situation of the major project of 863 project, the coast line of Vietnam is divided into five types, and the OLI image of Landsat 8 is used for optimal image segmentation. The image feature is based on the image intelligent classification technology, and a multi-level coastline intelligent interpretation model is constructed, and a variety of machine learning methods are adopted to further study the intelligent interpretation of the coast of the Vietnamese mainland. The main content and innovation of the paper are as follows: 1. aiming at the problem of scale selection in image segmentation, a method for acquiring an optimal segmentation scale based on knowledge feedback is proposed, and finally, the characteristic difference discrimination criterion is utilized to obtain the optimal segmentation scale from the scale alternative set. The experiment shows that the method can accurately acquire the optimal segmentation scale of a plurality of target objects according to the interpretation task. based on the obtained optimal segmentation scale, the classification of the object class and the hierarchical structure of information extraction are determined by combining the classification of the object class in combination with the clustering method. Multiple features based on OLI image definition are systematically analyzed and evaluated. based on the correlation between the characteristics of the Pearson sample correlation coefficient evaluation feature, the coefficient of variation is used to measure the information quantity carried by the characteristic, the correlation between the characteristic and the target land class is analyzed by using a single-factor variance analysis method, and the rules are interpreted according to the characteristics that are strongly related to the 10 ground objects. A feature selection method based on the reduction of the interpretation uncertainty is presented. The characteristics are preferably selected based on a plurality of methods: sensitivity analysis is carried out based on the weight value of the BP neural network, the importance of the characteristics is sorted through the size of the sensitivity coefficient, and the feature selection is performed; and from the aspects of reducing the classification uncertainty, enhancing the robustness of the rule expression, and the like, The influence of external factors and internal factors on the characteristic value is studied, the influence of the thin cloud in the image is studied in the external factors, and the influence of the image segmentation scale on the characteristic value of the image object is studied. The feature extraction of the original high-dimensional features is carried out using the factor analysis method to realize the dimension reduction. In this paper, the application effect of five types of commonly used machine learning algorithms in the classification of the coast features is analyzed, and the optimal parameter setting of each classifier is obtained through a series of experiments, and a machine learning model suitable for the classification of the coast features is constructed. The classification ability of each classifier to the ground object class is analyzed by the image classification experiment. The experiment shows that the classification accuracy of the SVM is very high for the 10 ground objects, and the classification effect is the best. The classification results of the two classifiers of the SVM and the random forest are complementary to each other. This paper presents a method of classification using the relation rules, which can be classified by comparing the relation between the attributes or the features, and it has the characteristics of easy to understand and stick. In this paper, the relationship between the image objects is constructed, and then the relationship rules of the three large classes of vegetation-related, water-related and sandstone are obtained through the machine learning, and the expert knowledge of the image interpretation is supplemented. The main conclusions are as follows: the total length of the coast of Vietnam in 2013 is about 4067km, the proportion of the artificial coastline is the largest, the proportion of the sludge line is the smallest, the distribution of the artificial coastline is the most, the sandy shore line is mainly distributed in the middle of the south part of the Vietnam, the shoreline of the bedrock is distributed in the middle of the Vietnam, the coastline of the mangrove is distributed on the two ends of the north and the south of the Vietnamese mainland, the muddy coast is distributed sporadically on the whole continental coastline, and the longest coastline of the 28 coastal provincial administrative regions is the Guangning province, The shortest is in the province of Nanding, the longest in the artificial line is the province of Nanding, the longest of the bedrock is the Qing and the province, the longest of the sandy shore is the smooth province, the longest is the Guangning province, and the longest of the mangroves is the province of Jinyi.
【学位授予单位】：解放军信息工程大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：P715.7
，
本文编号：2426479
[Abstract]:The coastline refers to the surface boundary between the average tide and the tide, which is the basic element of the topographic map and the chart. With the influence of natural and human factors, the change of the coastline, the rapid and accurate monitoring of the dynamic changes of the position and the nature of the coastline is of great significance to the rapid updating of the basic geographic information, the resource investigation and the scientific management of the coastal zone. The purpose of this paper is to study the method of fast and accurate acquisition of the coastline information from the remote sensing image. With the support of the comprehensive analysis and decision-making simulation of the South China Sea and its neighborhood space situation of the major project of 863 project, the coast line of Vietnam is divided into five types, and the OLI image of Landsat 8 is used for optimal image segmentation. The image feature is based on the image intelligent classification technology, and a multi-level coastline intelligent interpretation model is constructed, and a variety of machine learning methods are adopted to further study the intelligent interpretation of the coast of the Vietnamese mainland. The main content and innovation of the paper are as follows: 1. aiming at the problem of scale selection in image segmentation, a method for acquiring an optimal segmentation scale based on knowledge feedback is proposed, and finally, the characteristic difference discrimination criterion is utilized to obtain the optimal segmentation scale from the scale alternative set. The experiment shows that the method can accurately acquire the optimal segmentation scale of a plurality of target objects according to the interpretation task. based on the obtained optimal segmentation scale, the classification of the object class and the hierarchical structure of information extraction are determined by combining the classification of the object class in combination with the clustering method. Multiple features based on OLI image definition are systematically analyzed and evaluated. based on the correlation between the characteristics of the Pearson sample correlation coefficient evaluation feature, the coefficient of variation is used to measure the information quantity carried by the characteristic, the correlation between the characteristic and the target land class is analyzed by using a single-factor variance analysis method, and the rules are interpreted according to the characteristics that are strongly related to the 10 ground objects. A feature selection method based on the reduction of the interpretation uncertainty is presented. The characteristics are preferably selected based on a plurality of methods: sensitivity analysis is carried out based on the weight value of the BP neural network, the importance of the characteristics is sorted through the size of the sensitivity coefficient, and the feature selection is performed; and from the aspects of reducing the classification uncertainty, enhancing the robustness of the rule expression, and the like, The influence of external factors and internal factors on the characteristic value is studied, the influence of the thin cloud in the image is studied in the external factors, and the influence of the image segmentation scale on the characteristic value of the image object is studied. The feature extraction of the original high-dimensional features is carried out using the factor analysis method to realize the dimension reduction. In this paper, the application effect of five types of commonly used machine learning algorithms in the classification of the coast features is analyzed, and the optimal parameter setting of each classifier is obtained through a series of experiments, and a machine learning model suitable for the classification of the coast features is constructed. The classification ability of each classifier to the ground object class is analyzed by the image classification experiment. The experiment shows that the classification accuracy of the SVM is very high for the 10 ground objects, and the classification effect is the best. The classification results of the two classifiers of the SVM and the random forest are complementary to each other. This paper presents a method of classification using the relation rules, which can be classified by comparing the relation between the attributes or the features, and it has the characteristics of easy to understand and stick. In this paper, the relationship between the image objects is constructed, and then the relationship rules of the three large classes of vegetation-related, water-related and sandstone are obtained through the machine learning, and the expert knowledge of the image interpretation is supplemented. The main conclusions are as follows: the total length of the coast of Vietnam in 2013 is about 4067km, the proportion of the artificial coastline is the largest, the proportion of the sludge line is the smallest, the distribution of the artificial coastline is the most, the sandy shore line is mainly distributed in the middle of the south part of the Vietnam, the shoreline of the bedrock is distributed in the middle of the Vietnam, the coastline of the mangrove is distributed on the two ends of the north and the south of the Vietnamese mainland, the muddy coast is distributed sporadically on the whole continental coastline, and the longest coastline of the 28 coastal provincial administrative regions is the Guangning province, The shortest is in the province of Nanding, the longest in the artificial line is the province of Nanding, the longest of the bedrock is the Qing and the province, the longest of the sandy shore is the smooth province, the longest is the Guangning province, and the longest of the mangroves is the province of Jinyi.
【学位授予单位】：解放军信息工程大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：P715.7
，
本文编号：2426479
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