Building footprint extraction in remote sensing data
benefits many important applications, such as urban
planning and population estimation. Recently, rapid
development of Convolutional Neural Networks (CNNs) and
open-sourced high resolution satellite building image
datasets have pushed the performance boundary further for
automated building extractions. However, CNN approaches
often generate imprecise building morphologies including
noisy edges and round corners. In this paper, we leverage
the performance of CNNs, and propose a module that uses
prior knowledge of building corners to create angular and
concise building polygons from CNN segmentation outputs. We
describe a new transform, Relative Gradient Angle Transform
(RGA Transform) that converts object contours from time vs.
space to time vs. angle. We propose a new shape descriptor,
Boundary Orientation Relation Set (BORS), to describe angle
relationship between edges in RGA domain, such as
orthogonality and parallelism. Finally, we develop an
energy minimization framework that makes use of the angle
relationship in BORS to straighten edges and reconstruct
sharp corners, and the resulting corners create a polygon.
Experimental results demonstrate that our method refines
CNN output from a rounded approximation to a more clear-cut
angular shape of the building footprint.

Quantization in Relative Gradient Angle Domain For Building Polygon Estimation

PS T3.5: Computer Vision and Intelligent Systems

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Yuhao Chen

1

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Quantization in Relative Gradient Angle Domain For Building Polygon Estimation

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