Monitoring the built-up environment of Shanghai on the street-block level using SAR and volunteered geographic information

ABSTRACT

In this geo-statistical analysis of change detection, we illustrate the evolution of the built-up environment in Shanghai at the street-block level. Based on two TerraSAR-X image stacks with 36 and 15 images, covering the city centre of Shanghai for the time period from 2008 to 2015, a set of coherence images was created using a small baseline approach. The road network from Open Street Map, a volunteered geographic information product, serves as the input dataset to create street-blocks. A street-block is surrounded by roads and resembles a ground parcel, a real estate property – a cadastral unit. The coherence information is aggregated to these street-blocks for each observation and the variation is analysed over time. An analysis of spatial autocorrelation reveals clusters of similar behaviours. The result is a detailed map of Shanghai highlighting areas of change. We argue that the aggregation and grouping of synthetic aperture radar coherence image information to real-world entities (street-blocks) is comprehensible and relevant to the urban planning process. Therefore, this research is a contribution to the community of urban planners, designers, and government agencies who want to monitor the development of the urban landscape.     

A comparative data-fusion analysis of multi-sensor satellite images

Remote-sensing data play an important role in extracting information with the help of various sensors having different spectral, spatial and temporal resolutions. Therefore, data fusion, which merges images of different spatial and spectral resolutions, plays an important role in information extraction. This research investigates quality-assessment methods of multisensor (synthetic aperture radar [SAR] and optical) data fusion. In the analysis, three SAR data-sets from different sensors (RADARSAT-1, ALOS-PALSAR and ENVISAT-ASAR) and optical data from SPOT-2 were used. Although the PALSAR and the RADARSAT-1 images have the same resolutions and polarisations, images are gathered in different frequencies (L and C bands, respectively). The ASAR sensor also has C-band radar, but with lower (25 m) resolution. Since the frequency is a key factor for penetration depth, it is thought that the use of different SAR data might give interesting results as an output. This study describes a comparative study of multisensor fusion methods, namely the intensity-hue-saturation, Ehlers, and Brovey techniques, by using different statistical analysis techniques, namely the bias of mean, correlation coefficient, standard deviation difference and universal image quality index methods. The results reveal that Ehlers' method is superior to the others in terms of spectral and statistical fidelity.     

Error analysis of satellite attitude determination using a vision-based approach

Improvements in communication and processing technologies have opened the doors to exploit on-board cameras to compute objects’ spatial attitude using only the visual information from sequences of remote sensed images. The strategies and the algorithmic approach used to extract such information affect the estimation accuracy of the three-axis orientation of the object.This work presents a method for analyzing the most relevant error sources, including numerical ones, possible drift effects and their influence on the overall accuracy, referring to vision-based approaches. The method in particular focuses on the analysis of the image registration algorithm, carried out through on-purpose simulations. The overall accuracy has been assessed on a challenging case study, for which accuracy represents the fundamental requirement. In particular, attitude determination has been analyzed for small satellites, by comparing theoretical findings to metric results from simulations on realistic ground-truth data. Significant laboratory experiments, using a numerical control unit, have further confirmed the outcome.We believe that our analysis approach, as well as our findings in terms of error characterization, can be useful at proof-of-concept design and planning levels, since they emphasize the main sources of error for visual based approaches employed for satellite attitude estimation. Nevertheless, the approach we present is also of general interest for all the affine applicative domains which require an accurate estimation of three-dimensional orientation parameters (i.e., robotics, airborne stabilization).     

空间信息服务管理机制研究

针对UDDI、CSW、Clearinghouse等空间信息服务注册与查找方式存在的不足,提出了空间信息服务管理机制,集成实现空间信息服务的搜索、描述、分类、组织、存储、发布、发现为一体的空间信息服务管理架构。基于此,实现了空间信息服务描述与注册、服务发现与调用等服务活动过程,为空间信息服务共享与增值应用奠定基础。     

A new Likelihood Ratio for supervised classification of fully polarimetric SAR data: An application for sea ice type mapping

One of the potential applications of polarimetric Synthetic Aperture Radar (SAR) data is the classification of land cover, such as forest canopies, vegetation, sea ice types, and urban areas. In contrast to single or dual polarized SAR systems, full polarimetric SAR systems provide more information about the physical and geometrical properties of the imaged area. This paper proposes a new Bayes risk function which can be minimized to obtain a Likelihood Ratio (LR) for the supervised classification of polarimetric SAR data. The derived Bayes risk function is based on the complex Wishart distribution. Furthermore, a new spatial criterion is incorporated with the LR classification process to produce more homogeneous classes. The application for Arctic sea ice mapping shows that the LR and the proposed spatial criterion are able to provide promising classification results. Comparison with classification results based on the Wishart classifier, the Wishart Likelihood Ratio Test Statistic (WLRTS) proposed by Conradsen et al. (2003) and the Expectation Maximization with Probabilistic Label Relaxation (EMPLR) algorithm are presented. High overall classification accuracy of selected study areas which reaches 97.8% using the LR is obtained. Combining the derived spatial criterion with the LR can improve the overall classification accuracy to reach 99.9%. In this study, fully polarimetric C-band RADARSAT-2 data collected over Franklin Bay, Canadian Arctic, is used.     

基于高分辨率SAR图像成像机理的震害信息分析

与中低分辨率SAR图像相比,高分辨率SAR图像受目标复杂性和成像噪声等因素的干扰更为严重,致使应用高分辨率SAR图像检测震害目标变得更加困难.传统的适用于中低分辨率SAR图像的震害信息提取方法对于高分辨率SAR图像有一定的局限性.为了从高分辨率SAR图像中更加准确地提取震害信息,从SAR图像成像机理和目标的后向散射特征出发,对建筑物和道路、桥梁的震害特征进行了细致分析,为寻求合适的目标检测和提取方法提供了思路.     

Riegl VZ-400三维激光扫描仪数据的建模的研究

三维激光扫描技术是近年来发展起来的一门新技术,其在众多领域中有非常广阔的应用前景。本文介绍了三维激光扫描仪目前的应用现状,以及原理和数据后处理方法。最后通过青海玉树山体建模实例叙述了Riegl VZ-400扫描仪如何获取数据、点云数据的拼接及三维模型的建立。     

复杂地区机载SAR数据获取航线规划

本文主要论述机载SAR数据获取航线规划,提出了适合于地形复杂地区机载SAR航摄的航线规划方案。详细说明了航线设计方法,并引入低分辨率的DEM数据,对航线规划结果进行了模拟;最后对机载合成孔径雷达航摄进行三维模拟,以期更好地显示并检验航线规划结果。     

基于组合罗德里格矩阵的异常特征点探测

特征点是点云配准的基础,针对异常特征点对点云配准精度的影响,本文给出了一种异常特征点探测方法,即根据罗德里格矩阵的性质,对特征点进行向量组合,对比分析不同向量组合的点云配准精度,确定配准误差超限的向量组合,此向量组合中含有而其他向量组合中不合有的特征点,便是异常特征点.通过实例验证了该方法的可行性.     

Integration of high-resolution imagery and LiDAR data for object-based classification of urban area

This paper investigates the synergistic use of high-resolution multispectral imagery and Light Detection and Ranging (LiDAR) data for object-based classification of urban area. The main contribution of this paper is the development of a semi-automated object-based and rule-based classification method. In the implemented approach, the diverse knowledge about land use/land cover classes are transformed into a set of specialized rules. Further, this paper explores supervised Gaussian Mixture Models for classification, which have been primarily used for unsupervised classification. The work is carried out on test data from two different sites. Contribution of the LiDAR data resulted in a significant improvement of overall Kappa. Accuracy assessment carried out for aforementioned classification methods shows higher overall kappa for both the study sites.