A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis

Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasi- topological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis.     

Satellite-based monitoring and statistics for raft and cage aquaculture in China’s offshore waters

Based on Landsat 8 remote sensing images, a combination of an unsupervised classification algorithm and artificial review was used to extract areas for Chinese offshore raft and cage aquaculture in 2018. The results of the extraction showed that China’s 2018 coastal zone raft aquaculture area comprised 194,110 ha, of which the province having the largest raft aquaculture area was Jiangsu (28.77 %), followed by Fujian (20.42 %) and Shandong (13.11 %). The cage aquaculture area covered 57,847,799 square meters, of which the provinces with the largest cage aquaculture area were Fujian (64.81 %), Guangdong (17.45 %), and Liaoning (5.63 %). In addition, by combining high-resolution remote sensing image visual interpretation and field investigation, the classification of 1200 sample points in four regions was determined, and the accuracy of the aquaculture area extraction was found to be 87.35 %. The extraction results can be used not only to evaluate China’s aquaculture production but also offer significant reference value for scientific planning related to sea use, ecological environmental protection, and marine disaster prevention and mitigation.     

一种基于点云提取微地形对象的方法

在分析微地形特点以及现有网格DEM在表述微地形中存在的不足的基础上,提出了面向对象的数字高程模型,给出了该模型的形式化描述和以点云为数据源构建面向对象数字高程模型过程中采用的对象边界的识别、特征保持的点云疏化、基于属性近似的网格约化及对象识别等关键步骤的方法。并以实例证明了此过程的可行性。     

土地资源体多级变粒度空间数据管理方法

结合资源体的概念和土地要素功能提出了土地资源体概念,将土地资源数据对象组织成土地利用资源体、土地权属资源体等不同类型的土地资源体.在土地资源体对象基础上,结合具体的土地资源管理业务需要,提出了土地资源体多级变粒度数据组织管理模式,对土地资源体横向按资源体、图库、图层集合、图层和地物等不同资源授权粒度,纵向按多级区域层次管理的方式进行管理.国家、省、市级别的土地资源管理以县为基本单位,以区域特征管理国家、省、市、县等逻辑关系,县级的土地资源时空数据按资源体、图库、图层集合、图层和地物等不同资源授权粒度来对数据进行管理.在具体的土地资源管理和业务实施时,可以根据实际的需要对位于不同位置、不同层次、不同资源体类型的土地资源体进行动态组合.     

基于持久化的网吧管理系统数据库设计和实现

设计了一种健壮持久层体系结构,并运用该结构,根据对象/关系数据库映射（O/R Mapping）策略,设计和实现了基于持久化的网吧管理系统数据库。这种实现数据持久化的方案在C＋＋开发环境下,具有比较重要的应用价值。     

面向对象的空间数据采集

本文主要介绍在Turbo C++支撑下,用面向对象的方法,对地图的空间数据进行采集、分类、编辑、净化。该系统利用C++中面向对象的概念及类的继承与地图数据的层次关系相吻合的特点,使得数据的输出能满足更多的要求,并且针对地图数据量大的特点,引入了多态和动态覆盖相结合的方法,以满足内存和速度两方面的要求。     

用线特征摄影测量对目标体素进行量测和重建的可行性研究

本文利用线特征摄影测量方法对ＣＡＤ实体造型中体素进行自动量测与重建、借此建立ＣＡＤ与数字摄影测量的桥梁，文中提出了一种以描述体素几何元素为未知数，直接在像片线特征与物体表面之间进行匹配的线特征摄影测量模型和方法，并对其可行性进行了实验，实验结果表明：线特征摄影测量能达到立体摄影测量相同的，甚至更高的精度，从而可解决无明显目标点工点物体的自动量测。     

视景地形仿真及其应用

本文主要讨论了视景地形仿真的特点、数据基础及其实现的整个过程，列举了视景地形仿真在军事上和其它领域的应用实例。实验表明，本文所介绍的视景地形仿真过程简单而容易实现，仿真的地形景观具有良好的视觉效果。     

大地电磁二维层状模型参数化反演

本文针对目前二维反演存在求解参数过多和求其偏导数困难的两个问题，在近年来关于连续介质二维大地电磁测深资料快速反演的理论基础上，对二维层状模型反演的目标函数，进行线性近似，采用加权法来近似计算二维模型参数的偏导数。从而实现比较快速的二维层状模型的参数化反演。理论模型的反演试验表明，反演迭代可以较快地以足够的精度拟合视电阻率和相位资料，并收敛接近理论模型。     

大地电磁自适应正则化反演算法

针对大地电磁正则化反演中正则化因子的选取困难问题提出了自适应正则化反演算法（Adaptive Regularized Inversion Algorithm, ARIA）. 在该算法中， ①提出了一种新的数据方差处理方法：数据方差规范化，使得数据方差的大小只对数据的拟合发生影响，不对数据目标函数和模型约束目标函数的权重产生影响，从而减少了正则化因子取值的影响因素；②提出了粗糙度核矩阵的概念，并给出了由基本结构插值基函数计算粗糙度核矩阵的公式，使得模型目标函数的构建更为简便、直接；③根据数据目标函数、模型约束目标函数和正则化因子之间的关系，提出了两种正则化因子自适应调节方法. 本文详细阐述了最平缓模型约束下的大地电磁一维连续介质反演的ARIA实现，以几个算例的分析比较来说明ARIA的有效性.