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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Automatic Detection and Reconstruction of 2-D/3-D Building Shapes From Spaceborne TomoSAR Point Clouds
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Automatic Detection and Reconstruction of 2-D/3-D Building Shapes From Spaceborne TomoSAR Point Clouds

机译：从星载TomoSAR点云自动检测和重建2-D / 3-D建筑形状

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Modern spaceborne synthetic aperture radar (SAR) sensors, such as TerraSAR-X/TanDEM-X and COSMO-SkyMed, can deliver very high resolution (VHR) data beyond the inherent spatial scales of buildings. Processing these VHR data with advanced interferometric techniques, such as SAR tomography (TomoSAR), allows for the generation of four-dimensional point clouds, containing not only the 3-D positions of the scatterer location but also the estimates of seasonal/temporal deformation on the scale of centimeters or even millimeters, making them very attractive for generating dynamic city models from space. Motivated by these chances, the authors have earlier proposed approaches that demonstrated first attempts toward reconstruction of building facades from this class of data. The approaches work well when high density of facade points exists, and the full shape of the building could be reconstructed if data are available from multiple views, e.g., from both ascending and descending orbits. However, there are cases when no or only few facade points are available. This usually happens for lower height buildings and renders the detection of facade points/regions very challenging. Moreover, problems related to the visibility of facades mainly facing toward the azimuth direction (i.e., facades orthogonally oriented to the flight direction) can also cause difficulties in deriving the complete structure of individual buildings. These problems motivated us to reconstruct full 2-D/3-D shapes of buildings via exploitation of roof points. In this paper, we present a novel and complete data-driven framework for the automatic (parametric) reconstruction of 2-D/3-D building shapes (or footprints) using unstructured TomoSAR point clouds particularly generated from one viewing angle only. The proposed approach is illustrated and validated by examples using TomoSAR point clouds generated using TerraSAR-X high-resolution spotlight data stacks acquired from ascending orbit covering two differen- test areas, with one containing simple moderate-sized buildings in Las Vegas, USA and the other containing relatively complex building structures in Berlin, Germany.
机译：TerraSAR-X / TanDEM-X和COSMO-SkyMed等现代星载合成孔径雷达（SAR）传感器可以提供超出建筑物固有空间范围的超高分辨率（VHR）数据。使用SAR断层扫描（TomoSAR）等先进的干涉技术处理这些VHR数据，可以生成四维点云，不仅包含散射体位置的3-D位置，而且还包含对时空变形的估计。厘米或什至毫米的规模，使其对于从太空生成动态城市模型非常有吸引力。受这些机会的启发，作者较早提出了一些方法，这些方法证明了从此类数据重建建筑物立面的首次尝试。当存在高密度的外立面点时，这些方法效果很好，并且如果可以从多个视图（例如从上升轨道和下降轨道）获得数据，则可以重建建筑物的完整形状。但是，在某些情况下，没有或只有很少的立面点可用。这通常发生在较低高度的建筑物上，这使得对立面点/区域的检测非常困难。此外，与主要面向方位方向的立面（即，垂直于飞行方向取向的立面）的能见度有关的问题也可能导致难以获得单个建筑物的完整结构。这些问题促使我们通过开发屋顶点来重建建筑物的完整2D / 3D形状。在本文中，我们提出了一种新颖且完整的数据驱动框架，该框架用于使用非结构化TomoSAR点云（仅从一个视角生成）自动（参数）重建2-D / 3-D建筑形状（或占地面积）。通过使用TomoSAR点云实例演示并验证了所提出的方法，TomoSAR点云是使用TerraSAR-X高分辨率聚光数据堆栈生成的，该堆栈是从上升轨道获取的，覆盖两个不同的测试区域，其中一个包含美国拉斯维加斯的简单中型建筑，而其他包含德国柏林相对复杂的建筑结构。

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