基于DSM阴影仿真和高度场光线跟踪的影像阴影检测

介绍了一个建筑物阴影检测的模型。首先利用摄影测量学原理来计算阴影坐标。即用数字表面模型(digitalsurfacemodel,缩写为DSM)和太阳高度和方位来计算建筑物阴影的空间坐标,并由相机模型计算出每个阴影单元对应的扫描行和相机空间坐标。由高度场光线跟踪判断阴影的可见性,对可见阴影计算出它在投影图像上的坐标。然后在这个结果的基础上再对图像进行阴影的细分割。     

基于地表定量参数的沙漠化遥感监测方法

利用野外沙漠化调查的定位数据和ETM+ 遥感数据，在实验分析的基础上，探讨了沙漠化程度与地表参数之间的定量关系，即沙漠化与植被指数（NDVI）、地表辐射温度（LST）之间的关系，提出了综合反映沙漠化土地生物物理特征的遥感监测指数——沙漠化遥感监测差值指数（DDI），为沙漠化遥感监测提供有效的定量化方法。     

Outlier detection algorithms and their performance in GOCE gravity field processing

The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data.     

Development of a remotely sensed,historical land-cover change database for rural Chihuahua,Mexico

The purpose of this article is to describe the development of a remotely sensed, historical land-cover change database for the northwestern quarter of Chihuahua, Mexico, The database consists of multi-temporal land-cover classifications and change detection images. The database is developed to facilitate future investigations that examine urban–rural linkages as possible drivers of rural land-use and land-cover changes. To develop the needed land-cover change database, this study uses the North American Landsat Characterization (NALC) MSS triplicates because of their temporal depth and spatial breadth. Challenges exist, however, to effective classification and change detection using the NALC triplicates, including illumination differences across multiple scenes and periods caused by topographic and solar variations and the lack of ground reference data for historic periods. Therefore, creation of the database is a four step process. First, extensive pre-processing is performed to enhance comparability of multi-date images. Pre-processing includes topographic correction, mosaic creation and multi-date radiance normalization. Second, ancillary sources of land-cover data are combined with visual interpretations of enhanced images to define reference pixels used to classify the images using the maximum likelihood algorithm. Third, classification accuracy is assessed. Fourth, post-classification change detection is performed. Results indicate significant image improvements after pre-processing that permit very good overall classification (86.26% classified correctly) and change detection. To conclude, findings are presented that indicate significant changes to arid grasslands/shrublands and forest resources in mountainous regions.     

Particle size dependent response of aerosol counters

During an international workshop at the Institute for Experimental Physics of the University of Vienna, Austria, which was coordinated within the Committee on Nucleation and Atmospheric Aerosols (IAMAS-IUGG), 10 instruments for aerosol number concentration measurement were studied, covering a wide range of methods based on various different measuring principles. In order to investigate the detection limits of the instruments considered with respect to particle size, simultaneous number concentration measurements were performed for monodispersed aerosols with particle sizes ranging from 1.5 to 50 nm diameter and various compositions.The instruments considered show quite different response characteristics, apparently related to the different vapors used in the various counters to enlarge the particles to an optically detectable size. A strong dependence of the 50% cutoff diameter on the particle composition in correlation with the type of vapor used in the specific instrument was found. An enhanced detection efficiency for ultrafine hygroscopic sodium chloride aerosols was observed with water operated systems, an analogous trend was found for n-butanol operated systems with nonhygroscopic silver and tungsten oxide particles.     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

Estimation of azimuth and slowness of tele-seismic signals recorded by a local seismic network

Introduction The azimuth and slowness are two major features of seismic signals. The accurate estimation of them is quite important for both phase identification and event location. Generally, there are two types of seismic stations, i.e. 3-component stations (3C) and arrays. To estimate the two direc-tional parameters, the polarization analysis (Jurkevics, 1988) is commonly used for 3C stations and the frequency-wavenumber spectrum analysis ( f-k) (Capon, 1969; Kvaerna, Doornbos, 1986) is …     

由地方台网记录确定远震信号的方位角和慢度

介绍一种新的适合于地方遥测台网数据处理的方法，估算远震信号到达台站的方位角和慢度．该方法是根据信号在各台站上的到时与台站位置矢量在信号传播方向上的投影之间的相关性的原理．实际分析结果表明，应用该方法对地方台网的记录进行处理时可以准确地计算出信号的方位角和慢度，并能准确快捷地对地方台网记录的远震信号进行解释．      

克孜尔水库大坝的面波检测

1999年 3月 15日克孜尔地震之后对克孜尔水库坝体出现的裂缝进行了面波勘探。首先介绍了面波勘探法的基本原理 ,然后通过典型剖面检测与资料处理找出异常区、带并进行解释。试验表明面波勘探法适用于克孜尔水库大坝监测     

基于GIS的黄河上游龙羊峡库区生态环境遥感监测研究

本项研究以黄河上游生态环境严重退化的龙羊峡库区为研究区，应用遥感与GIS一体化的方法，建立了库区生态环境动态监测信息系统。在系统的支持下，利用多时相、多波段TM遥感数据，对库区近十年来生态环境变化进行监测分析，揭示了库区土地沙漠化、水土流失、土地利用／土地覆盖的现状，以及建库近十年来生态环境时空演化的过程与发展趋势，为库区生态环境治理提供科学依据。本项研究利用遥感和GIS技术，采用生态环境综合分类系统，在一次分类处理中通过多层次不同级别的组合获得了基于同一信息源的土地沙漠化、草场盖度、土地利用处地覆盖等多种环境类型信息，并利用环境信息模型对库区的土壤侵蚀进行了评价。基于GIS的分类后再处理使遥感分类精度有了一定程度的提高。研究表明，在GIS的支持下充分利用遥感信息监测与评价生态环境，是区域生态环境定量化研究的有效途径。