用星载合成孔径雷达(SAR)立体影像生成DEM的方法研究

星载SAR成像是一种微波成像,由于微波具有穿透云雾、雨雪等的特点,使得SAR能够不受天气和时间的限制而对地球扫描成像,这使得SAR具有一般光学成像所没有的优点。人们正积极探索把SAR数据用于测绘并来解决一些问题。考虑到卫星在短时间内的运动比较平稳,并且它的相对关系精确度高,本文主要从SAR成像的几何角度和两景影像的相对关系出发,分析了雷达成象的特点,进而探讨了从立体像对上提取DEM的方法,并就具体步骤做了进一步的阐述。     

Acquisition of Directional Parameters in Aerial Images Based on DEM Data

1　Extraction of the steep curve mapThe steep curve is defined as connection linefrom zero cross points which are produced fromthe convolution of Laplace ( LOG) filtration toDEM. Firstly, the elevation data from DEM areprocessed with LOG convolution and zero cross.The sampling formula is 2G(i,j) =12πσ6(r2 -2σ2)e-r2/2σ2 (1)where -M≤i≤M,-M≤j≤M,r2=i2+j2; M isthe filtration radius, nearly equaling to 5.1σ2; pre diction errorσis set to 4. Suppose that e[i,j] is asmall …     

倾斜影像的量测方法研究

随着“数字地球”“数字城市”的提出,能够快速获取空间数据基础信息成为当今急需解决的问题之一。传统的航空摄影测量主要是对地物顶部信息的获取,而对侧面信息获取比较困难,因此倾斜航空测量技术成为国内外快速发展起来的一门高新技术。本文主要论述了基于倾斜影像量测的几种方法,并对以后量测方法的发展趋势进行了说明。     

高分辨率卫星影像立体测图分析研究

运用高分辨率卫星遥感影像立体像对,利用少量的外业控制点,进行l：10000比例尺地形图立体测图精度试验,获取分辨率更高的彩色影像。     

VirtuoZo全数字摄影测量工作站处理数码影像的生产应用

全数字摄影测量主要应用于生产数字地形图及数字地面高程模型（DEM）和数字正射影像图（DOM）。某市及周边地区1：l000地形图航测项目,利用DMC一110摄影仪进行摄影获取的影像数据,使用VirtuoZo数字摄影测量工作站,阐述进行航测内业绘制地形图的全面流程。DMC技术将发挥更大的优势,在数字摄影测量中得到越来越广泛的应用。     

一种基于紫外极光图像的亚暴膨胀期起始时刻的自动检测方法

准确界定亚暴起始时刻是理解亚暴相关问题的关键.已有研究主要集中在两方面:一是从极光图像中人工挑选亚暴事件进行案例分析或统计分析来研究亚暴发生机制及亚暴期间的地磁环境;二是基于一些空间物理参数,如AE指数、SME(SuperMAG electrojet)指数、Pi2、正弯扰等,采用人眼判断或是模式识别的方法从中找出亚暴起始时刻.本文尝试采用模式识别的方法从紫外极光图像中自动地检测出亚暴膨胀期起始时刻.首先,将紫外极光图像通过网格化处理转换到磁地方时-地磁纬度(MLT-MLAT)直角坐标下,然后通过模糊c均值聚类方法提取亮斑,再考察亮斑强度是否增强、面积是否极向膨胀来判断是不是亚暴事件.本文方法在1996年12月-1997年2月这三个月的Polar卫星紫外极光图像上进行了实验验证.我们将检测到的亚暴起始时刻与Liou(J. Geophys. Res., 2010, 115: A12219)的人工标记进行了对比,并详细分析了与标记不一致的多检和漏检事件.本文提出的自动检测方法可以快速地从海量紫外极光图像中完成亚暴事件的初步筛选,方便研究人员进一步深入研究极光亚暴.     

Improving georeferencing accuracy of Very High Resolution satellite imagery using freely available ancillary data at global coverage

ABSTRACT

While impressive direct geolocation accuracies better than 5.0?m CE90 (90% of circular error) can be achieved from the last DigitalGlobe’s Very High Resolution (VHR) satellites (i.e. GeoEye-1 and WorldView-1/2/3/4), it is insufficient for many precise geodetic applications. For these sensors, the best horizontal geopositioning accuracies (around 0.55?m CE90) can be attained by using third-order 3D rational functions with vendor’s rational polynomial coefficients data refined by a zero-order polynomial adjustment obtained from a small number of very accurate ground control points (GCPs). However, these high-quality GCPs are not always available. In this work, two different approaches for improving the initial direct geolocation accuracy of VHR satellite imagery are proposed. Both of them are based on the extraction of three-dimensional GCPs from freely available ancillary data at global coverage such as multi-temporal information of Google Earth and the Shuttle Radar Topography Mission 30?m digital elevation model. The application of these approaches on WorldView-2 and GeoEye-1 stereo pairs over two different study sites proved to improve the horizontal direct geolocation accuracy values around of 75%.     

应用遥感影像制作矿区大比例尺地形图

本文在专业的遥感和地理信息系统软件平台支持下,利用Aster立体像对和IKONOS高分辨率遥感影像分别提取矿区地形信息和地物信息,结合矿区的实际特点,参照地形图制作规范对矿区大比例尺地形图的制作进行了分析和探讨,结果表明利用遥感影像进行矿区大比例尺地形图制作不但周期短、速度快、时效性强,而且精度完全能够满足生产需要,是一种实用可行的方法。     

一种GIS空间知识分析的影像纹理尺度提取算法

本文提出了一种基于GIS领域空间知识的影像纹理尺度提取方法:该方法分析了GIS领域知识辅助下人类对纹理特征的认知过程,对4个步骤的具体实现算法进行探讨;并选取海南省昌江县作为研究区域,实验证明了本方法的有效性,且与传统的枚举法相比,在性能相近的情况下其效率显著提高,更适合于大数据量遥感影像分类运算。     

Auto-identification of forest fire-points in NOAA images based on neural network

Identification of forest fire-points in NOAA images in the basis of monitoring forest fire using NOAA satellite data. Traditional visual interpretation is difficult to settle for auto identification with computer. The artificial neural network technique provides a new means for solving this problem. In this paper, the principles and method of using neural network technique to automatically identify fire-points in NOAA images are discussed and the test in the range of Hubei province is presented. The result of the test shows that the disciplined neural network has collected the character of fire-points and has ability to identify fire-points in NOAA images. Comparing neural network with visual interpretation, the conclusion is drawn that by using neural network the purpose of auto-identification of forest fire-points in NOAA images can be realized with the almost same precision.