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

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 b...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ying Sun [verfasserIn] Xinchang Zhang [verfasserIn] Xiaoyang Zhao [verfasserIn] Qinchuan Xin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	building boundary extraction convolutional neural network active contour model high resolution optical images LiDAR

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 10(2018), 9, p 1459
Übergeordnetes Werk:	volume:10 ; year:2018 ; number:9, p 1459

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs10091459

Katalog-ID:	DOAJ014160080

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source	In Remote Sensing 10(2018), 9, p 1459 volume:10 year:2018 number:9, p 1459
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author	Ying Sun
spellingShingle	Ying Sun misc building boundary extraction misc convolutional neural network misc active contour model misc high resolution optical images misc LiDAR misc Science misc Q Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model
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topic_title	Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model building boundary extraction convolutional neural network active contour model high resolution optical images LiDAR
topic	misc building boundary extraction misc convolutional neural network misc active contour model misc high resolution optical images misc LiDAR misc Science misc Q
topic_unstemmed	misc building boundary extraction misc convolutional neural network misc active contour model misc high resolution optical images misc LiDAR misc Science misc Q
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title	Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model
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title_full	Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model
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title_sort	extracting building boundaries from high resolution optical images and lidar data by integrating the convolutional neural network and the active contour model
title_auth	Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model
url	https://doi.org/10.3390/rs10091459 https://doaj.org/article/0cbd1826b1d64bdbbbd55ca220a52896 http://www.mdpi.com/2072-4292/10/9/1459 https://doaj.org/toc/2072-4292
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Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model

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