A Multi-Feature Fusion-Based Change Detection Method for Remote Sensing Images

An object-oriented change detection method for remote sensing images based on multiple features using a novel weighted fuzzy c-means (WFCM) method is presented. First, Gabor and Markov random field textures are extracted and added to the original images. Second, objects are obtained by using a watershed segmentation algorithm to segment the images. Third, simple threshold technology is applied to produce the initial change detection results. Finally, refining is conducted using WFCM with different feature weights identified by the Relief algorithm. Two satellite images are used to validate the proposed method. Experimental results show that the proposed method can reduce uncertainties involved in using a single feature or using equally weighted features, resulting in higher accuracy.     

Semi Automatic Road Extraction by Fusion of High Resolution Optical and Radar Images

Two new methods for fusion of high-resolution optical and radar satellite images have been proposed to extract roads in high quality in this paper. Two fusion methods, including neural network and knowledge-based fusion are introduced. The first proposed method consists of two stages: (i) separate road detection using each dataset and (ii) fusion of the results obtained using a neural network. In this method, the neural networks are separately applied on high-resolution IKONOS and TerraSAR-X images for road detection, using a variety of texture parameters. The outputs of two neural networks, as well as the spectral features of optical image, are used in a third neural network as inputs. The second method is a knowledge-based fusion using thresholds of narrow roads and vegetation gray levels. First roads are extracted from each source separately. The outputs are then compared and advantages and disadvantages of each data source are investigated . The results obtained from accuracy assessment show the efficiency of the proposed methods. Furthermore, the comparison of the results showed the superiority of the first algorithm.     

基于Skyline黄河流域3维景观的建立

以ADS 40航空影像为数据源,利用Leica数字摄影测量系统制作黄河流域的正射影像及DEM;利用3维GIS软件Skyline将DOM叠加到DEM上构建3维场景;利用3DMAX进行桥梁的精细建模并进行格式转换;利用数码相机对建筑物、桥梁和堤坝的侧面纹理进行采集并进行纹理映射,实现了黄河流域3维景观的可视化。     

A combined single range and single image device for low-cost measurement of building façade features

The dimensions of building façades and window apertures are usually determined by making direct measurements using tapes and plummets. This approach, however, has a number of drawbacks including the physical risk to which the persons making the measurements are exposed. This paper proposes an indirect approach based on close range photogrammetry that is inexpensive, simple, fast and safe, and which does not require specialist staff or direct ground control measurements. The method is based on taking a picture using a digital camera and measuring the distance to the object using a handheld laser distance meter. Both items of equipment are mounted on a specially designed support that allows the laser distance meter to move independently of the camera.     

基于等效残差积探测粗差的方差-协方差分量估计

已有方差-协方差分量估计(VCE)的粗差探测是对残差检验,而VCE的本质是利用残差的二阶量来估计它的二阶中心矩,因此更合理的方法应该对残差二阶量检验.由最小二乘残差估值导出了一组服从标准正态分布的等效残差;然后从等效残差的VCE基本方程出发,分别采用χ2统计量和正态积Np统计量检验等效残差的平方及其乘积.结果表明,采用...     

Generating and mapping population density surfaces within a geographical information system

Some alternative ways of presenting maps of spatial population distribution are examined using northern Leicestershire, England, as an example. The authors conclude that dasymetric and isarithmic maps are a desirable alternative and can be relatively easily generated using satellite imagery in a geographical information system.     

PPP网解UPD模糊度固定技术监测尼泊尔Ms8.1级地震对中国珠峰地区及周边地震同震位移

利用UPD模糊度固定技术无需顾及基线解算基站地震所带来的影响,可以进行高精度非差PPP解算,"真实"获取地震周边地区GNSS站点高精度同震位移变化。为此,本文利用"国家基准一期工程""中国大陆构造环境监测网络"以及国家测绘地理信息局在珠峰周边所观测的GNSS观测资料,基于UPD模糊度固定技术高精度非差解算2015年4月25日尼泊尔Ms8.1级地震对我国珠峰地区及周边地震同震位移影响。首先,本文选取全国及周边IGS均匀分布、站点稳定、远离震区的GNSS连续观测网络数据计算卫星端的宽、窄巷UPD,采用PPP网解UPD模糊度固定技术,对解算地震区域内的GNSS测站的载波相位模糊度进行固定,得到无模糊度的精确相位观测值,进行高精度非差PPP解算;通过对平静日IGS测站数据处理与ITRF2008历元坐标对比分析,验证了该方法的精确性;最后,对2015年4月25日、5月12日地震以及地震前后数据,进行了UPD模糊度固定技术的非差PPP解算,分析了中国珠峰地区及周边GNSS站的同震位移;同时也分析了中国珠峰地区在2005—2015年10年的位移变化情况。UPD模糊度固定技术整网解算的方法也证实了能够为GNSS用于监测地震同震位移等,提供了一种精确、可靠的技术手段。     

利用多面函数拟合和欧拉矢量法建立福建省GPS速度场模型

通过处理福建省卫星导航定位基准服务系统(FJCORS)2013-2015年观测资料,获得GPS站实测运动速度值,在此基础上,分别利用多面函数拟合法和欧拉矢量法构建了福建省及周边区域GPS速度场模型．内符合精度方面,多面函数拟合的速度精度南北分量为1.06 mm／a、东西分量为2.00 mm／a;欧拉矢量法得到的速度精度南北分量为1.36 mm／a、东西分量为1.22 mm／a．外符合精度方面,两种方法模拟得到的检核点速度值与实测速度值的较差均在±2.28 mm／a以内．在福建省陆域内,两种方法得到的速度场具有高度的一致性,运动速度均为35 mm／a左右,运动方向为SE-E向．      

Mathematical Modelling and Accuracy Testing of Spot Level 1B Stereopairs

During a project concerned with DEM generation using SPOT stereoscopic imagery, difficulties were experienced when using Level 1B stereopairs for the task. This paper presents a mathematical solution to overcome this problem which has been implemented at the University of Glasgow. Results are included from experimental tests which were carried out over a test field in Jordan using this solution. These tests used five SPOT Level 1B stereopairs together with a single SPOT Level 1A stereopair for comparative purposes. The residual errors at the ground control points and independent check points are given and show that a satisfactory solution was achieved.     

工程放样中三角高程的精度分析

三角高程放样中,平面位置放样与高程放样都采用经纬仪,是一种快速的方法,但距离误差及高差误差与竖直角有很大的关系,本文对此进行了分析。同时分析了测距仪的放样精度。     

多分类SVM主动学习及其在遥感图像分类中的应用

针对应用传统分类器和被动学习的方法,难以满足遥感图像处理实际应用的要求这一困境,提出了一种新的基于多分类SVM的主动学习方法,与被动学习的随机选择不同,主动学习是在少量标记类别的初始训练样本集基础上,通过反复迭代主动学习的方式,得到最有利SVM分类器性能的样本为支持向量。研究表明,这种方法直接避免了大量的计算,可有效地减少样本训练时需要标记样本的数目,并取得较为理想的分类效果。     

高分辨率SAR影像道路交叉口自动提取方法

针对高分辨率SAR影像道路交叉口提取易受周围地物干扰的问题,设计了一种利用三角形检测模型提取道路交叉口的方法。该方法首先采取滤波与锐化消除道路交叉口区域噪声点并凸显道路边缘;接着根据灰度特征,通过形态学运算确定道路交叉口候选区域;最后在候选道路交叉口区域运用三角形检测模型,确认道路交叉口。使用3 m Tan DEM-X数据进行实验,结果证明算法对各种常见二维道路交叉口模型均有较好识别效果。     

Empirical modeling and spatio-temporal patterns of urban evapotranspiration for the Phoenix metropolitan area,Arizona

In this study, an empirical model for predicting urban evapotranspiration (ET) is examined for the Phoenix metropolitan area that is in a subtropical desert climate using in situ ET measurements from a local flux tower and remotely sensed moderate-resolution imaging spectroradiometer land products. Annual ET maps of Phoenix are then created for the period from 2001 to 2015 using the empirical model developed. A time-series trend analysis is finally performed using predicted ET maps to discover the spatio-temporal patterns of ET changes during the study period. Results suggest that blue-sky albedo and land surface temperature are two statistically significant variables explanatory to model urban ET for Phoenix. Areas that have experienced significant increases of ET are highly spatially clustered, and are mainly found on the outskirts of the city, while areas of decreasing ET are generally associated with highly developed areas, such as downtown Phoenix.     

小波变换块自适应量化算法压缩SAR原始数据

本文研究了如何使用基于小波变换的块自适应量化（WT-BAQ）算法压缩合成孔径雷达原始数据。结合一组实测SAR原始数据,进行了压缩和解压缩,并计算了数据域及图像域评价参数,给出了压缩算法最终所成的图像。并与块自适应量化（BAQ）、块自适应矢量量化（BAVQ）以及小波包变换块自适应量化（WPT-BAQ）方法进行了性能比较。实验表明,在相同比特率条件下,本文算法得到的数据域信噪比和图像域信噪比均比另外三种算法高。本文算法具有一定实用价值。最后文章给出了结论和将来进一步的工作。     

基于ARIMA模型的地球自转变化预报

准确预报地球自转变化对于精密定位、空间飞行器的跟踪与正常运行,具有重要的科学意义和实用价值。根据周日变化（UT1-UTC）和极移变化（PM）的特性,用最小二乘法,建立了适合于UT1-UTC和PM趋势项和周期项观测数据的拟合模型。对于UT1-UTC残差序列采用差分自回归移动平均（ARIMA）模型进行预报,对于PM残差采用季节性自回归移动平均（SARMA）模型进行预报。实例结果表明：我们的UT卜UTC预报结果比地球自转服务（IERS）产品好,而PM比IERS要差一些。当大气角动量（AAM）和海洋角动量（OAM）数据参与计算后,对UT卜UTC的预报有细微改善,对PM无改善。     

利用遥感技术监测船舶航速的方法研究

主要讨论了两种利用遥感技术提取水面运动船舶的航速方法：利用波高谱信息提取航速和利用船尾迹中的横波信息提取航速。作者介绍了这两种方法的原理，描述了利用遥感技术提取船舶航速的工作流程，并利用模拟和实测的船尾迹波高数据对这两种方法进行了验证。从计算结果看，两种方法精度都能满足仅要求航速量级的需求者，利用波高谱信息的方法误差（10％左右）小于利用横波信息的方法误差（20％左右）。该项研究为实时或近实时探测运动船舶航速提供了新的方法。     

Tree Detection in Urban Regions Using Aerial Lidar and Image Data

In this letter, we present an approach to detecting trees in registered aerial image and range data obtained via lidar. The motivation for this problem comes from automated 3-D city modeling, in which such data are used to generate the models. Representing the trees in these models is problematic because the data are usually too sparsely sampled in tree regions to create an accurate 3-D model of the trees. Furthermore, including the tree data points interferes with the polygonization step of the building roof top models. Therefore, it is advantageous to detect and remove points that represent trees in both lidar and aerial imagery. In this letter, we propose a two-step method for tree detection consisting of segmentation followed by classification. The segmentation is done using a simple region-growing algorithm using weighted features from aerial image and lidar, such as height, texture map, height variation, and normal vector estimates. The weights for the features are determined using a learning method on random walks. The classification is done using the weighted support vector machines, allowing us to control the misclassification rate. The overall problem is formulated as a binary detection problem, and the results presented as receiver operating characteristic curves are shown to validate our approach     

Detection of gullies in roughly textured terrain using airborne laser scanning data

Airborne laser scanning data contain information about surface features, some of which are of subtle form. These features are usually embedded within the terrain, and rarely form distinct shape-transition to their surroundings. While some efforts have been made in extracting linear elements from laser scanning data, attention was mostly turned to dominant elements that are very clear and distinct. We present in this paper a detection model for gullies of various dimensions using airborne laser scanning data. Gullies are regarded as one of the main landform-reshaping agents, having a pejorative effect on the environment and on regional development. They are commonly observed along receding lakes as a common response to water-level drop. The paper demonstrates how a multi-scale approach enables the extraction of various gully forms, from well developed to subtle. It then proposes an optimization driven model for handling fragmentation in the detection. Results show that using the proposed model, gully networks can be reconstructed and ∼30 cm deep features can be identified and separated from their surroundings using moderate point density data.     

Automated Digital Photogrammetry on a Shoestring

A system capable of automatically deriving the three dimensional co-ordinates of well defined binarized targets is described. Significantly, the system comprises cheap "off the shelf" hardware and software, requiring a minimum of development. Comparative tests using imagery obtained with a Hasselblad semi-metric camera and a 35 mm SLR camera are presented. By means of automated sub-pixel measurement, the results achieved are equivalent to those undertaken through manual target measurement using a first order analytical plotter.     

Detection,segmentation and classification of 3D urban objects using mathematical morphology and supervised learning

We propose an automatic and robust approach to detect, segment and classify urban objects from 3D point clouds. Processing is carried out using elevation images and the result is reprojected onto the 3D point cloud. First, the ground is segmented and objects are detected as discontinuities on the ground. Then, connected objects are segmented using a watershed approach. Finally, objects are classified using SVM with geometrical and contextual features. Our methodology is evaluated on databases from Ohio (USA) and Paris (France). In the former, our method detects 98% of the objects, 78% of them are correctly segmented and 82% of the well-segmented objects are correctly classified. In the latter, our method leads to an improvement of about 15% on the classification step with respect to previous works. Quantitative results prove that our method not only provides a good performance but is also faster than other works reported in the literature.