Presentation + Paper
12 April 2021 Building height estimation via satellite metadata and shadow instance detection
Author Affiliations +
Abstract
Estimating building height from satellite imagery is important for digital surface modeling while also providing rich information for change detection and building footprint detection. The acquisition of building height usually requires a LiDAR system, which is not often available in many satellite systems. In this paper, we describe a building height estimation method that does not require building height annotation. Our method estimates building height using building shadows and satellite image metadata given a single RGB satellite image. To reduce the data annotation needed, we design a multi-stage instance detection method for building and shadow detection with both supervised and semi-supervised training. Given the detected building and shadow instances, we can then estimate the building height with satellite image metadata. Building height estimation is done by maximizing the overlap between the projected shadow region given a query height and the detected shadow region. We evaluate our method on the xView2 and Urban Semantic 3D datasets and show that the proposed method achieves accurate building detection, shadow detection, and height estimation.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hanxiang Hao, Sriram Baireddy, Emily Bartusiak, Mridul Gupta, Kevin LaTourette, Latisha Konz, Moses Chan, Mary L. Comer, and Edward J. Delp "Building height estimation via satellite metadata and shadow instance detection", Proc. SPIE 11729, Automatic Target Recognition XXXI, 117290L (12 April 2021); https://doi.org/10.1117/12.2585012
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KEYWORDS
Satellites

Earth observing sensors

Satellite imaging

Multispectral imaging

Image segmentation

LIDAR

Statistical analysis

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