【作者】 王常颖；

【导师】 倪岳峰； 张杰；

【作者基本信息】 中国海洋大学 ， 环境科学， 2009， 博士

【摘要】 本文在“908海岛海岸带遥感调查”课题的支持下,引入数据挖掘技术,开展了海岸线提取方法和海岸带土地利用分类方法等研究。对于海岸线提取方法研究,针对课题调查内容,海岸线划分为人工岸线、基岩岸线、砂质岸线、粉沙淤泥质岸线和生物岸线等五种类型,本文在设计海岸线提取方法时,首先分析了不同类型岸线的特征,然后基于数据挖掘中的关联规则技术,分别提出了相应五种岸线类型的提取方法。为了验证本文岸线提取方法的有效性,选取30m分辨率的Landsat TM/ETM+影像开展了实验研究,提取结果都优于3个像元。本文提出的岸线提取方法算法直观、容易实现,而且岸线提取连续。另外,为了对海岸线提取精度进行评价,提出了一种线段匹配精度评价方法。对于海岸带地物分类方法研究,本文以课题调查中的土地利用调查分类体系为例开展研究,提出了两种地物分类方法,基于证据理论的面向对象的遥感影像海岸带地物分类方法和面向对象的高分辨率影像海岸带地物分层分类方法。针对海岸带遥感调查中的土地利用II级地物分类需要,引入了证据理论的思想,提出了基于证据理论的面向对象的遥感影像海岸带地物分类方法。为了验证方法的有效性,选取10m分辨率的SPOT影像为数据源,综合考虑了光谱、纹理和形状等特征,在鸭绿江口区域进行了实验研究,分类精度达到了80%以上。该方法解决了单个特征属性类型识别时的不确定性问题,提高了分类精度。另外,为了能够处理研究区域已知样本空间不完备的情况,对基本的证据理论进行了推广。针对调查中土地利用III级地物分类需要,提出了面向对象的高分辨率影像海岸带地物分层分类方法。为了验证方法的有效性,选取0.61m分辨率的Quickbird影像为数据源,在众多光谱、纹理、形状和邻居特征中,应用关联规则技术,首先找到最能够区分各种地物类型的特征属性及其相关规则,然后基于这些挖掘出来的规则实现最终的分类,分类结果精度超过了80%。该方法解决了分类结果精度对专家经验的依赖,实现了精细地物的自动分类。更多还原

【Abstract】 Supposed by 908 Projects of Marine comprehensive investigation and assessment in China, in the view of data mining technique, coastline interpretation method and coastal land covers classification method from remote sensing images are studied.For coastline interpretation method, since shoreline are divided several types, such as artificial shoreline, bedrock shoreline, arenaceous shoreline, silt soreline and biologic shoreline, characters of different shorelines are analysed firstly and five kinds of coastline interpretation methods of the corresponding five types are designed. To verify the presented methods, experiments are implemented by Landsat TM/ETM+ images with the image resolution 30m. The precisions overmatch three pixels. Our proposed methods are intuitionistic and easy to realize. Moreover, the interpretation coastlines are consecutive. In addition, a coastline interpretation accuracy assessment algorithm is proposed to evaluate the experiment results.For coastal land covers classification method, aiming at the increasing resolution of remote sensing images, two classification methods are designed. One is object-oriented coastal land covers classification method based on evidence theory, the other is object-oriented fine land covers stratified classification method based on data mining.Aiming at II-level land covers type of remote sensing investigation, introducing the idea of evidence theory, coastal land covers classification method based on evidence theory is presented. To verify the method, SPOT image with 10m resolution is used. Considering spectrum, texture, shape and neighbour features synthetically, an experiment is implemented. The accuracy beyonds 80%. The proposed method solves the uncertainty of classification by single attribute, which improve the accuracy of classification. In addition, the basic D_S evidence theory is extended to process the incompleted sample space.Aiming at III-level land covers type of remote sensing investigation, object-oriented fine land covers stratified classification method based on data mining is proposed. To verify the presented method, Quickbird image with 0.61m resolution is used. From numerous features of spectrum, texture, shape and neighbour, by using association rule technique, attributes that can distinguish each land covers type are discovered firstly. Then basing on the attributes knowledge, the classification is implemented, which the accuracy of result beyonds 80%. This method eliminates the dependence to systematizer, which is an automatic classification method.更多还原