Matching of Persistent Scatterers to buildings
Matching of Persistent Scatterers to buildings
复制标题
持久散射体与建筑物的匹配
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
U. Soergel
中科院分区：
文献类型：
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作者：
A. Schunert;L. Schack;U. Soergel
持续散射体干涉测量（PSI）旨在估计一组足够稳定的雷达目标（称为持续散射体（PS））的地形和变形。因此，利用SAR图像的堆叠来区分感兴趣的信号和由例如大气条件随时间的变化引起的干扰。可实现的采样密度主要取决于所采用的传感器的分辨率和手头的场景的特性（即足够稳定的反射器的数量）。如果使用最高分辨率的星载合成孔径雷达数据，则可以实现非常高的PS密度，从而有可能实现建筑物底层结构的测绘。这提供了一个大的和全新的可能的应用范围。例如，可以使用PSI监控单个建筑物。但随之也出现了新的问题和挑战。其中两个是：哪些建筑物的采样密度足够用于监测目的，PS对应于哪种实际建筑结构？为了调查这些问题，PS结果与建筑轮廓保持一致。这可以很容易地绘制建筑物的PS密度，这部分回答了第一个问题。不可否认，PS的分布和建筑物的形状及其结构也起着作用。但是，PS密度图肯定有助于评估哪些结构可以充分监测。实际建筑结构和PS之间的对应关系是一个很难回答的问题。在这项工作中，我们试图提高地理编码的PS的GIS数据的帮助下，鉴于这可能是非常有帮助的给定的任务。
Persistent Scatterer Interferometry (PSI) aims at estimating the topography and deformation for a set of sufficiently stable radar targets, referred to as Persistent Scatterers (PS). Thereby, a stack of SAR images is exploited to distinguish between signal of interest and disturbances caused by, for instance, changes of the atmospheric conditions over time. The achievable sampling density mainly depends on the resolution of the employed sensor, and the characteristics of the scene at hand (i.e. the number of sufficiently stable reflectors). In case space-borne SAR data of the highest resolution is used, very high PS densities, potentially enabling the mapping of structures at sub-building level, can be achieved. This offers a big and completely new range of possible applications. For instance, it is possible to monitor single buildings using PSI. But with this also new questions and challenges arise. Two of those would be: which buildings are sampled dense enough for monitoring purposes, and to which actual building structure does the PS correspond? In order to investigate those questions, the PS results are aligned with building outlines. This easily enables to map the PS density of buildings, which partly answers the first question. Admittedly, also the distribution of the PS and the shape of the building as well as its structure play a role. But a map of the PS density would be definitely helpful to assess which structures can be monitored adequately. The correspondence between actual building structures and PS is a good deal harder to answer. In this work, we attempt to improve the geocoding of the PS with the help of the GIS data, in view of the fact that this may be very helpful for the given task.