Mapping building structures is crucial for environmental change and impact assessment, and is especially important to accurately estimate fossil fuel CO₂ emissions from human settlements. In this regard, the objective of this study is to develop novel and robust methods using time-series data acquired from Sentinel-1 synthetic aperture radar (SAR) to identify and map persistent building structures from coastal plains to high plateaus, as well as on the sea surface. From annual composites of SAR data in the two-dimensional VV-VH polarization space, we determined the VV-VH domain for detecting building structures, whose persistence was defined based on the number of times that a pixel was identified as a building in time-series data. Moreover, the algorithm accounted for misclassified buildings due to water-tree interactions in radar signatures and due to topography effects in complex mountainous landforms. The methods were tested in five cities (Bạc Liêu, Cà Mau, Sóc Trăng, Tân An, and Phan Thiết) in Vietnam located in different socio-environmental regions with a range of urban configurations. Using in-situ data and field observations, we validated the methods and found that the results were accurate, with an average false negative rate of 10.9% and average false positive rate of 6.4% for building detection. The algorithm could also detect small houses in rural settlements and in small islands such as in Hòn Sơn and Hòn Tre. Over sea surfaces, the algorithm effectively identified lines of power poles connecting islands to the mainland, guard shacks in marine blood clam farms in Kiên Giang, individual wind towers in the off-shore wind farm in Bạc Liêu, and oilrigs in the Vũng Tàu oil fields. The new approach was developed to be robust against variations in SAR incidence and azimuth angles. The results demonstrated the potential use of satellite dual-polarization SAR to identify persistent building structures annually across rural–urban landscapes and on sea surfaces with different environmental conditions.

中文翻译：

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越南水平地形和海面的建筑结构图

绘制建筑结构图对于环境变化和影响评估至关重要，对于准确估算化石燃料 CO ₂尤为重要来自人类住区的排放。在这方面，本研究的目的是使用从 Sentinel-1 合成孔径雷达 (SAR) 获取的时间序列数据开发新颖且稳健的方法，以识别和绘制从沿海平原到高原以及海面。从二维 VV-VH 极化空间中每年合成的 SAR 数据，我们确定了用于检测建筑结构的 VV-VH 域，其持久性是根据一个像素在时间上被识别为建筑物的次数来定义的 -系列数据。此外，该算法还考虑了由于雷达特征中的水树相互作用以及复杂山地地形中的地形效应而导致错误分类的建筑物。这些方法在五个城市（Bạc Liêu、Cà Mau、Sóc Trăng、Tân An、和 Phan Thiết）位于越南的不同社会环境区域，具有一系列城市配置。使用现场数据和现场观察，我们验证了这些方法，发现结果是准确的，建筑物检测的平均误报率为 10.9%，平均误报率为 6.4%。该算法还可以检测农村居民点和 Hòn Sơn 和 Hòn Tre 等小岛上的小房子。在海面上，该算法有效地识别了连接岛屿和大陆的电线杆、Kiên Giang 海洋血蛤养殖场中的守卫棚屋、Bạc Liêu 海上风电场中的单个风塔以及 Vũng Tàu 油田中的石油钻井平台领域。新方法的开发是为了对 SAR 入射角和方位角的变化具有鲁棒性。