1995—2010年静止卫星云量数据检验和评价

对利用FY2和GMS静止气象卫星建立的东亚地区气候数据集(EAGSCDR FY2 and GMS Geostationary Satellite Climate Data Record over East Asia)进行了检验和评估,使用的检验源数据包括中国地面气候资料与国际卫星云气候计划ISCCP D2月平均云量数据集.对由上述3种不同观测手段得到的多年平均总云量的空间分布特征分析结果表明:3种资料的总云量分布形势有较好的一致性,但是在40°N以北地区,ISCCP和EAGSCDR得到的总云量在量值上高于地面观测值.用地面观测资料检验华南及长江流域EAGSCDR的云检测产品的结果表明,总的准确率为82.10％,总漏判率6.85％,总误判率为11.05％,秋冬季节准确率偏低.EAGSCDR与ISCCP云量都是由卫星资料处理得到的,二者差异主要来自算法的不同,检验结果表明,EAGSCDR中的云量产品精度优于ISCCP云量,并且其时间分辨率可达到1 h,空间分辨率达到5 km,由此可见,EAGSCDR的云产品比ISCCP云产品更有优势.     

Estimation of parameters in a split functional model of geodetic observations (<Emphasis Type="Italic">M</Emphasis><Subscript>split</Subscript> estimation)

The paper presents a method of estimating parameters in two competitive functional models. The models considered here are concerned with the same observation set and are based on the assumption that an observation may result from a realization of either of two different random variables. These variables differ from one another at least in the main characteristic (for example, outliers can be realizations of one variable). A quantity that describes the opportunity of identifying a single observation with one random variable is assumed to be known. That quantity, called the elementary split potential, is strictly referred to the amount of information that an observation can provide about two competitive assumptions concerning the observation distribution. Parameter assessments that maximize the global elementary split potential (concerning all observations), are called M _split estimators. A generalization of M _split estimation presented in the paper refers to the theoretical foundation of M-estimation. An erratum to this article can be found at     

基于云理论、粗集和模糊神经网络的区域橡胶种植适宜度评估模型

针对橡胶种植适宜性评估,基于云理论、粗集理论和模糊神经网络理论,提出了一种适宜度评估模型。该模型将转化的样本数据进行粗集简约,通过模糊神经网络得出评价因子的隶属函数,计算评价等级。研究结果表明,此模型能够科学、快速、准确地分析出橡胶种植最适宜区、适宜区、次适宜区和不适宜区。     

沙漠地区无人机影像连接点提取

提出了一种纹理自适应的影像连接点提取方法,有针对性地解决沙漠地区无人机影像对比度低、相似性强、纹理信息不均衡,经常会造成连接点过少甚至提取、匹配失败的突出问题。该方法利用影像金字塔进行粗匹配,建立影像之间近似单应变换关系;通过对原始影像分块计算灰度共生矩阵的熵来评估影像纹理指标,并据此实现特征点的自适应提取与匹配。实验表明,该方法能够得到数量较多、分布均匀、可靠性强的同名点。     

基于中性集对北川羌族自治县新城人工建设用地的识别

人工建设用地(包括建筑物、道路、广场等社会服务设施)的识别一直是用来监测地区发展速度的一个有效途径。针对目前在人工建设用地识别领域中对在建建筑物的忽视问题,利用中性集、均值漂移以及绿度因子等概念将在建建设用地信息进行增强,进而将其成功识别出来。实验证明,该方法对高分辨率遥感影像的人工建设用地识别是可行的。通过分析2009—2013年期间北川新城的建设工地面积及分布的变化情况可以看出,北川新城在2010—2013年期间完工面积占2009—2010年新建工程总面积的98.17%,在北川新城拓展区又新建0.6 km2的工程,施工迅速,为受灾居民提供了良好的居住和生活保障。     

Development of a data mining method for land control

Land resources are facing crises of being misused, especially for an intersection area between town and country, and land control has to be enforced. This paper presents a development of data mining method for land control. A vector-match method for the prerequisite of data mining i. e., data cleaning is proposed, which deals with both character and numeric data via vectorizing character-string and matching number. A minimal decision algorithm of rough set is used to discover the knowledge hidden in the data warehouse. In order to monitor land use dynamically and accurately, it is suggested to set up a real-time land control system based on GPS, digital photogrammetry and online data mining. Finally, the means is applied in the intersection area between town and country of Wuhan city, and a set of knowledge about land control is discovered.     

遥感数据空间尺度分级模型与基本比例尺关系

多源遥感数据综合应用是遥感发展的必然趋势,统一的遥感数据空间尺度分级模型是多源数据集成与综合应用的基础。虽然已有多种空间尺度分级模型,但很多主流模型并非出于分尺度综合应用目的,缺乏客观的比较和评价。国家基本比例尺系统作为经过论证、中国应用面最广泛、接受度最高的一种尺度分级系统,是以应用为导向的遥感数据空间尺度分级模型的最优参照系。从不同视觉精度下国家基本比例尺对图像空间分辨率的需求出发,比较各空间尺度分级模型的层级分辨率与需求分辨率的匹配情况,包括OGC Well Known Scale Set的Global CRS84Pixel和Google Maps Compatible,以及NASA World Wind、Google Map、百度地图、天地图等软件平台采用的层级格网系统,以及"五层十五级"遥感数据组织模型,通过对数据信息冗余度的分析,对各个模型进行了评价。结果表明,在高视觉精度应用需求下,"五层十五级"模型与基本比例尺精度要求具有较明显的匹配优势,其次为OGC Google Maps Compatible模型和天地图模型,其余模型平均数据冗余倍数在2倍左右;在低视觉精度应用需求下,"五层十五级"模型平均数据冗余度仍为最低,其次为Google Map模型,其余模型平均数据冗余倍数都在2倍以上。     

应用描述词汇约简的OGC地理信息服务演绎推理

针对OGC地理信息服务在地理空间知识的有效组织和表达方面的能力比较弱,缺乏对服务信息的描述,数据丰富而知识缺乏,致使已有的数据在知识的表示和检索上存在缺陷,通过引入地理本体,按照基础地理信息要素分类,对地理信息服务的关键描述词汇进行提取,构建了地理信息服务本体库和实例数据库,应用粗糙集理论建立关键描述词汇约简模型,基于该模型实现了对地理信息服务实例数据库进行知识约简,形成最优实例数据库。最后应用地理信息服务本体库和实例数据库,基于演绎推理模型开发实例原型系统,实现了对地理信息服务对象的语义检索和推理,并通过试验从查全率和查准率两个指标验证了该方法的可行性、有效性和准确性。     

粗糙集理论进行遥感图像监督分类的样本质量评价的研究

In the supervised classification process of remotely sensed imagery, the quantity of samples is one of the important factors affecting the accuracy of the image classification as well as the keys used to evaluate the image classification. In general, the samples are acquired on the basis of prior knowledge, experience and higher resolution images. With the same size of samples and the same sampling model, several sets of training sample data can be obtained. In such sets, which set reflects perfect spectral characteristics and ensure the accuracy of the classification can be known only after the accuracy of the classification has been assessed. So, before classification, it would be a meaningful research to measure and assess the quality of samples for guiding and optimizing the consequent classification process. Then, based on the rough set, a new measuring index for the sample quality is proposed. The experiment data is the Landsat TM imagery of the Chinese Yellow River Delta on August 8th, 1999. The experiment compares the Bhattacharrya distance matrices and purity index zl and △x based on rough set theory of 5 sample data and also analyzes its effect on sample quality.     

基于粗规则对象空间信息表的最小规则集生成

提出了粗规则对象空间信息表的概念、相关概念和性质，并给出实现粗规则最小化算法。基于此，编写了相应的计算机程序，并对一个地理信息系统实例规则进行了研究。实例表明运行可靠、结果正确，可应用于智能决策信息系统的知识库优化。