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.     

Seasonal Variation of MODIS Vegetation Indexes and Their Statistical Relationship With Climate Over the Subtropic Evergreen Forest in Zhejiang, China

Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day 1-km vegetation index products, daily temperature, photosynthetically active radiation (PAR), and precipitation from 2001 to 2004 were utilized to analyze the temporal variations of the MODIS normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), as well as their correlations with climate over the evergreen forested sites in Zhejiang-a humid subtropical region in the southeast of China. The results showed that both NDVI and EVI could discern the seasonal variation of the evergreen forests. Attributed to the sufficient precipitation in the study area, the growth of vegetation is mainly controlled by energy; as a result, NDVI, and especially EVI, is more correlated with temperature and PAR than precipitation. Compared with NDVI, EVI is more sensitive to climate condition and is a better indicator to study vegetation variations in the study region     

An Efficient Reordering Prediction-Based Lossless Compression Algorithm for Hyperspectral Images

In this letter, we propose an efficient lossless compression algorithm for hyperspectral images; it is based on an adaptive spectral band reordering algorithm and an adaptive backward previous closest neighbor (PCN) prediction with error feedback. The adaptive spectral band reordering algorithm has some strong points. It can adaptively determine the range of spectral bands needed to be reordered, and it can efficiently find the optimum branches. Hyperspectral images have a large number of spectral bands, which express the same land cover structure and have high correlation. The adaptive backward PCN prediction with error feedback can sufficiently make use of this correlation. Experiments show that implementing both the reordering of the spectral bands before prediction and the prediction with error feedback improve compression performance     

国土资源数据分析及其整合与集成

本文详细分析了国土资源数据特点、综合数据来源、产生方式和使用方法等方面,对数据进行了分类,比较了不同的数据格式。在此基础上,阐述了国土资源数据的集成框架,讨论了数据整合和集成的方法和流程,提出了数据整合与集成的三种框架问题。     

应用GIS技术的台风灾害危险性评估系统

由于台风灾害危险性评估是一个复杂的多源信息综合分析的过程,借助功能强大、操作简便和显示直观的ArcGIS作为开发平台,开发了台风灾害危险性评估系统,实现了台风灾害危险性评估的可视化,发挥GIS强大的空间信息可视化管理和分析功能,为台风防灾减灾提供决策服务.     

长沙市景观破碎化时空分异特征研究

基于2000年长沙市1:10万土地利用图,利用ERDAS软件对1989年、2006年的遥感图像进行解译;在地理信息系统软件ArcGIS和景观格局分析软件Fragstats支持下,以城镇建设空间重心为中心,分别选取东西、南北景观样带(40 km×8 km)和8个不同半径的同心圆缓冲区,用斑块密度、边界密度、平均斑块分维数...     

A study of supervised classification accuracy in fuzzy topological methods

The multiple classifier system (MCS) is an effective automatic classification method, useful in connection with remote sensing analysis techniques. Combining MSC with induced fuzzy topology enables a decomposition of image classes. This fuzzy topological MCS then provides a new and improved approach to classification. The basic classification methods discussed in this paper include maximum likelihood classification (MLC), minimum distance classification (MIND) and Mahalanobis distance classification (MAH).     

方向数据插值方法研究

方向数据是矢量数据的标量表示,通常标量数据的插值方法不适用于方向数据。本文根据方向数据的特征,把其分解成沿坐标轴的分量,通过对分量数据的插值形成表面,最后合成反算出方向数据,给出了一种对于方向数据的行之有效的插值方法;并用实验进行了验证,对其插值结果和精度进行了统计和分析,取得了较为理想的内插结果。     

基于Oracle数据库的正射影像数据的组织和管理

随着航空摄影技术以及卫星遥感技术的迅速发展.数字正射影像已经越来越为社会的广大用户所利用。为了使数字正射影像数据更好的应用于国民经济各个部门,就需要对影像数据提供一个有效的合理的数据组织和管理的方式。本文分析了Oracle数据库所提供的数据类型的特点,对基于大型数据库的正射影像数据组织和管理做了较为详尽的阐述。     

房产测量中椭圆和样条曲线的多段线逼近

本文介绍了在AutoCAD环境中,对椭圆和样条曲线以面积为逼近指标调整偏移距离,用B样条函数处理偏移后样条曲线的控制数据,生成多段线逼近原椭圆或样条曲线的方法.解决了房产测量和建筑设计中将椭圆和椭圆弧精确地转化为多段线的问题.