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Remote Sens. 2018, 10(9), 1459; https://doi.org/10.3390/rs10091459

Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model

1
Department of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
2
Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China
3
School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
4
Guangzhou Urban Planning and Design Survey Research Institute, Guangzhou 510060, China
*
Authors to whom correspondence should be addressed.
Received: 18 July 2018 / Revised: 30 August 2018 / Accepted: 11 September 2018 / Published: 12 September 2018
(This article belongs to the Special Issue Remote Sensing based Building Extraction)
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Abstract

Identifying and extracting building boundaries from remote sensing data has been one of the hot topics in photogrammetry for decades. The active contour model (ACM) is a robust segmentation method that has been widely used in building boundary extraction, but which often results in biased building boundary extraction due to tree and background mixtures. Although the classification methods can improve this efficiently by separating buildings from other objects, there are often ineluctable salt and pepper artifacts. In this paper, we combine the robust classification convolutional neural networks (CNN) and ACM to overcome the current limitations in algorithms for building boundary extraction. We conduct two types of experiments: the first integrates ACM into the CNN construction progress, whereas the second starts building footprint detection with a CNN and then uses ACM for post processing. Three level assessments conducted demonstrate that the proposed methods could efficiently extract building boundaries in five test scenes from two datasets. The achieved mean accuracies in terms of the F1 score for the first type (and the second type) of the experiment are 96.43 ± 3.34% (95.68 ± 3.22%), 88.60 ± 3.99% (89.06 ± 3.96%), and 91.62 ±1.61% (91.47 ± 2.58%) at the scene, object, and pixel levels, respectively. The combined CNN and ACM solutions were shown to be effective at extracting building boundaries from high-resolution optical images and LiDAR data. View Full-Text
Keywords: building boundary extraction; convolutional neural network; active contour model; high resolution optical images; LiDAR building boundary extraction; convolutional neural network; active contour model; high resolution optical images; LiDAR
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sun, Y.; Zhang, X.; Zhao, X.; Xin, Q. Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model. Remote Sens. 2018, 10, 1459.

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