基于多平台的输电线路选线系统开发

输电线路选线是电力工程勘测设计的重要工作内容,传统的勘测手段存在地图不能及时更新,优化选线不合理,实地踏勘工作量大、前期工作周期长等问题。本文以在线卫星地图数据为主要载体,开发了基于多平台在线地理信息数据的输电线路工程选线系统,改变传统的作业模式,发挥多平台在线卫星地图数据的优势,大大提高输电线路设计效率和质量,克服了多平台地理信息数据不能转换,图形不能编辑、输出、量测等问题。     

西藏冈底斯成矿带矿化蚀变提取与成矿分析——以尼玛县帮勒地区为例

冈底斯成矿带是我国极具找矿潜力的地带,但受自然条件限制,该地带矿产资源开发程度普遍偏低。选取冈底斯成矿带北部尼玛县帮勒地区为研究区,以Landsat-8OLI数据为主要信息源,采用主成分分析法提取研究区铁染和羟基矿化蚀变信息。在GIS平台上,将提取的蚀变信息结合研究区现有地质、化探、矿产等资料进行综合分析,依据成矿理论,圈定2个成矿远景区。经野外验证,文中获取的矿化蚀变信息分布状况与已知矿点和已探明蚀变带的空间一致性高。矿化蚀变带的探明能够为区域矿产资源开发与勘查提供帮助。     

结合Gabor纹理与几何特征的高分辨率遥感影像城区道路提取方法

在充分考虑道路频谱在不同频带、不同方向上能量分布的基础上,提出一种基于Gabor纹理与几何特征相结合的高分辨率遥感影像城区道路提取方法。首先通过Gabor滤波器组得到遥感影像不同频带、不同方向上的Gabor纹理特征,并利用K-means方法对遥感影像进行分割;然后利用形态学方法分割与道路相连的地物,并选取适当的几何特征剔除非道路地物;最后利用形态学方法对道路网进行修整。实验结果表明,该方法可以有效、便捷地从高分辨率遥感影像中提取城区主干道路网。     

一种提取遥感影像中道路信息的方法

由于合理有效地选择分割阈值较难,因此基于阈值分割的道路信息提取方法对于遥感影像中含有多种类型道路、或非道路地物干扰比较明显的情况适用性不理想。为此,提出了一种将Mean Shift和阈值分割相结合的方法来提取道路信息。首先,采用Mean Shift方法对遥感影像进行平滑处理,在较好地保持道路边缘信息的同时使道路内部的纹理分布更加均匀;其次,对平滑处理后的影像进行Mean Shift分割处理,将具有相同或相似灰度值的道路用一种灰度值显示;然后,选择灰度直方图中像素数量值高的线所对应的灰度值作为分割区间边界点进行多阈值分割,得到初始道路信息;最后,对初始道路信息进行后处理,得到最终的道路信息。实验证明,该方法可以完成对遥感影像中道路信息的提取,拓宽了阈值分割方法提取道路信息的使用范围。     

高山冰川多时相多角度遥感信息提取方法

提出一种多角度遥感影像的冰川信息提取方法。通过"全域—局部"的阈值分割方法获取短时期内不同时相的遥感影像的冰雪边界,结合地形信息和多时相遥感影像的太阳角度信息,联合消除山体阴影对冰川的遮挡,并以多期影像的最小冰雪边界作为最佳冰川边界。以托木尔峰西侧冰川为研究对象,采用2009—2010年4个时相的遥感影像提取冰川信息。结果表明多角度遥感提取的冰川边界效果好,能有效地排除积雪与山体阴影的干扰。     

高分辨率遥感图像道路分割算法

为了提高从高分辨率遥感图像(high-resolution remote sensing image,HRI)中提取道路信息的自动化程度和准确性,发展了一种HRI道路分割算法,主要包括光谱合并、边界合并和基于形状特征的道路区域提取等3个步骤。其中,前2个步骤是基于区域生长的图像分割算法。光谱合并综合考虑了区域的均值、方差等统计特征量,以提高分割精度;边界合并采用了基于矢量梯度的边界计算方法,以准确提取多光谱HRI中的边界强度;结合全局最优合并算法实现光谱和边界合并,以得到最优化的分割结果。在道路区域被完整分割出来的基础上,利用形状特征提取道路,采用圆形度特征区分道路和非道路。利用2景Orb View3多光谱图像进行道路提取实验的结果表明,该方法的道路提取结果总精度和Kappa系数分别在97%和0.8以上,明显优于SVM监督分类方法。     

A GIS-based approach for detecting pollution sources and bioavailability of metals in coastal and marine sediments of Chabahar Bay,SE Iran

Chabahar Bay in SE of Iran is a shallow semi-enclosed environment affected by anthropogenic activities. In this paper, 19 sediment samples were collected and concentration of selected metals (Cu, Pb, Zn, Cd, Ni, Cr, Co, V and Fe) was determined using ICP-MS analytical method. Sediment samples from five stations were also selected for sequential extraction analysis and concentration of metals in each fraction was determined using ICP-OES. In order to investigate the environmental quality of Chabahar Bay, geographic information system (GIS) along with geochemical data, environmental indices and statistical analyses were used. Calculated contamination degree (C_d) revealed that most contaminated stations (Ch3, S1 and S3) are located SE of Chabahar Bay and contamination decreases in a NW direction. The S9 station, west of the bay, is also contaminated. High organic matter (OM) content in the sediments is most likely the result of fuel and sewage discharge from fishing vessels along with discharge of fishing leftovers. Significant correlation coefficient among OM, Fe, Cu, Pb, Zn and Cd seemingly reflects the importance of the role that OM and Fe oxy-hydroxides play in the metals mobility. The results of hierarchical cluster analysis (HCA), computed correlation coefficient and sequential extraction analysis suggest that Cu, Pb, Zn and Cd probably come from antifouling and sea vessel paints, while Ni, Cr, Co, V and Fe are most likely contributed by ophiolitic formations located north of the bay and/or deep sea sediments. Average individual contamination factors (ICFs) indicated that the highest health hazard from the bay is posed by Cu, Pb and Zn.     

天文学英语新词自动提取系统

科技名词中文译名标准化和规范化工作是推动我国科技进步和科学知识传播的重要基础。新的科学概念和技术名词层出不穷,如何及时发现并确定新生术语的中文译名是一项普遍的社会需求。介绍了一套全新的天文学英语新词自动提取系统。该系统综合使用脚本过滤、术语识别、正则表达匹配等多种方法,能够自动追踪Ar Xiv论文数据库的更新,分析天文学论文的内容,生成推荐术语列表,从而将学科专家从繁重的科技新词收集整理工作中解放出来,把有限的精力集中到更能体现专业素养的新词审定工作当中。这个系统将为推动天文学等基础学科的新词收集,乃至学科标准化等工作发挥积极作用。     

极不充分采动条件下地表下沉规律及计算方法研究

随着煤矿开采的不断加深,地表呆动程度(开采宽度D与开采深度H之比DIH)不断降低,并逐渐趋向于极不充分采动,同样长度的工作面对地表的影响程度极不同于浅部开采。因此,需要重新认识深部开采时的地表下沉规律。极不充分采动条件下地表下沉规律具有特殊性。经过对国内外大量观测资料的综合分析,发现当开采宽度小于地表下沉起动距时(极不充分采动),地表的下沉或地表下沉率(q′／q)为零。当开采宽度达到地表移动起动距时,地表下沉缓慢增长;达到临界采动距时,地表下沉急剧增大。地表下沉率(q′／q)与开采宽、深比D／H之间为负指数函数关系。在极不充分采动条件下进行地表下沉预计时必须对概率积分法的下沉系数进行修正。算例证明了本文给出计算方法的可行性。但这种修正方法如何与概率积分法相统一仍需进一步研究。     

Terrain Pattern Recognition and Spatial Decision Making for Regional Slope Stability Studies

A terrain partition scheme is presented that allows the identification of regions with high landslide risk in natural terrain zones on the basis of geomorphometric criteria from moderate resolution DEMs. The key factor being the terrain segmentation to aspect regions (regions formed by points preserving the same aspect direction) instead of using an artificial regular-grid terrain partition scheme. The study area is in western Greece (NW Peloponnesus) whereas a moderate resolution digital elevation model with spacing 75 m is used. Landslide inventory analysis and knowledge conceptualization identified that the landslide susceptibility of a particular aspect region is high, if the mean elevation is low and the mean gradient is high. Each aspect region was parametrically represented on the basis of its mean gradient and elevation. The domain of each parameter was divided to seven slices (classes) on the basis of the observed density. Subsequent knowledge based mapping identified aspect regions with high landslide susceptibility for the following spatial rule: (a) “mean slope in class 6 or 7” and (b) “mean elevation in class 1 to 5”. Alternatively the rule is expressed as mean slope to be equal or greater than 15^∘ whereas mean elevation to be in the range 0 to 750 m. These identified zones correspond to regions where historical landslides occurred (populated coastal areas in the North) as well as to south regions (natural terrain zone) where no landslide record is available, because of the limitations posed by the natural terrain landslide mapping program in Greece. The presented terrain segmentation technique combined to the spatial decision-making process, provided both an object framework for integrating geomorphometric parameters and a method for landslide risk analysis in natural terrain zones.