Large-scale Building Height Estimation from Single VHR SAR image Using Fully Convolutional Network and GIS building footprints
Height reconstruction of large-scale buildings from single very high resolution (VHR) SAR image is of great interest especially in applications with temporal restrictions. The problem is highly challenging due to the inherent complexity of SAR images, e.g., side-looking geometry and different microwave scattering contributions. In this work, we present a framework to estimate large-scale building heights from single VHR SAR image. The individual buildings are defined by GIS data, and deep neural network is used to segment wall area in SAR image. The wall layover length is then converted to height and assigned to each building footprint. Experiment in center Berlin area shows results of overall instance height accuracy around 3.51 meters.
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Large-scale Building Height Estimation from Single VHR SAR image Using Fully Convolutional Network and GIS building footprints
Semantic Scholar · Engineering · 2019
Abstract
Height reconstruction of large-scale buildings from single very high resolution (VHR) SAR image is of great interest especially in applications with temporal restrictions. The problem is highly challenging due to the inherent complexity of SAR images, e.g., side-looking geometry and different microwave scattering contributions. In this work, we present a framework to estimate large-scale building heights from single VHR SAR image. The individual buildings are defined by GIS data, and deep neural network is used to segment wall area in SAR image. The wall layover length is then converted to height and assigned to each building footprint. Experiment in center Berlin area shows results of overall instance height accuracy around 3.51 meters.