Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network

In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and...
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Autor*in:	Jianfeng Zhu [verfasserIn] Lichun Sui [verfasserIn] Yufu Zang [verfasserIn] He Zheng [verfasserIn] Wei Jiang [verfasserIn] Mianqing Zhong [verfasserIn] Fei Ma [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	point cloud classification semantic segmentation airborne laser scanning convolutional neural network deep learning

Übergeordnetes Werk:	In: ISPRS International Journal of Geo-Information - MDPI AG, 2012, 10(2021), 7, p 444
Übergeordnetes Werk:	volume:10 ; year:2021 ; number:7, p 444

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/ijgi10070444

Katalog-ID:	DOAJ071768564

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allfields	10.3390/ijgi10070444 doi (DE-627)DOAJ071768564 (DE-599)DOAJd7f43e67cd454dbe80fb811e09f09407 DE-627 ger DE-627 rakwb eng G1-922 Jianfeng Zhu verfasserin aut Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be directly applied to point cloud classification because of the disordered and unstructured characteristics of point clouds. In this paper, we design a novel convolution operator to extract local features directly from unstructured points. Based on this convolution operator, we define the convolution layer, construct a convolution neural network to learn multi-level features from the point cloud, and obtain the category label of each point in an end-to-end manner. The proposed method is evaluated on two ALS datasets: the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 3D Labeling benchmark and the 2019 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest (DFC) 3D dataset. The results show that our method achieves state-of-the-art performance for ALS point cloud classification, especially for the larger dataset DFC: we get an overall accuracy of 97.74% and a mean intersection over union (mIoU) of 0.9202, ranking in first place on the contest website. point cloud classification semantic segmentation airborne laser scanning convolutional neural network deep learning Geography (General) Lichun Sui verfasserin aut Yufu Zang verfasserin aut He Zheng verfasserin aut Wei Jiang verfasserin aut Mianqing Zhong verfasserin aut Fei Ma verfasserin aut In ISPRS International Journal of Geo-Information MDPI AG, 2012 10(2021), 7, p 444 (DE-627)689130961 (DE-600)2655790-3 22209964 nnns volume:10 year:2021 number:7, p 444 https://doi.org/10.3390/ijgi10070444 kostenfrei https://doaj.org/article/d7f43e67cd454dbe80fb811e09f09407 kostenfrei https://www.mdpi.com/2220-9964/10/7/444 kostenfrei https://doaj.org/toc/2220-9964 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 10 2021 7, p 444
spelling	10.3390/ijgi10070444 doi (DE-627)DOAJ071768564 (DE-599)DOAJd7f43e67cd454dbe80fb811e09f09407 DE-627 ger DE-627 rakwb eng G1-922 Jianfeng Zhu verfasserin aut Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be directly applied to point cloud classification because of the disordered and unstructured characteristics of point clouds. In this paper, we design a novel convolution operator to extract local features directly from unstructured points. Based on this convolution operator, we define the convolution layer, construct a convolution neural network to learn multi-level features from the point cloud, and obtain the category label of each point in an end-to-end manner. The proposed method is evaluated on two ALS datasets: the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 3D Labeling benchmark and the 2019 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest (DFC) 3D dataset. The results show that our method achieves state-of-the-art performance for ALS point cloud classification, especially for the larger dataset DFC: we get an overall accuracy of 97.74% and a mean intersection over union (mIoU) of 0.9202, ranking in first place on the contest website. point cloud classification semantic segmentation airborne laser scanning convolutional neural network deep learning Geography (General) Lichun Sui verfasserin aut Yufu Zang verfasserin aut He Zheng verfasserin aut Wei Jiang verfasserin aut Mianqing Zhong verfasserin aut Fei Ma verfasserin aut In ISPRS International Journal of Geo-Information MDPI AG, 2012 10(2021), 7, p 444 (DE-627)689130961 (DE-600)2655790-3 22209964 nnns volume:10 year:2021 number:7, p 444 https://doi.org/10.3390/ijgi10070444 kostenfrei https://doaj.org/article/d7f43e67cd454dbe80fb811e09f09407 kostenfrei https://www.mdpi.com/2220-9964/10/7/444 kostenfrei https://doaj.org/toc/2220-9964 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 10 2021 7, p 444
allfields_unstemmed	10.3390/ijgi10070444 doi (DE-627)DOAJ071768564 (DE-599)DOAJd7f43e67cd454dbe80fb811e09f09407 DE-627 ger DE-627 rakwb eng G1-922 Jianfeng Zhu verfasserin aut Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be directly applied to point cloud classification because of the disordered and unstructured characteristics of point clouds. In this paper, we design a novel convolution operator to extract local features directly from unstructured points. Based on this convolution operator, we define the convolution layer, construct a convolution neural network to learn multi-level features from the point cloud, and obtain the category label of each point in an end-to-end manner. The proposed method is evaluated on two ALS datasets: the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 3D Labeling benchmark and the 2019 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest (DFC) 3D dataset. The results show that our method achieves state-of-the-art performance for ALS point cloud classification, especially for the larger dataset DFC: we get an overall accuracy of 97.74% and a mean intersection over union (mIoU) of 0.9202, ranking in first place on the contest website. point cloud classification semantic segmentation airborne laser scanning convolutional neural network deep learning Geography (General) Lichun Sui verfasserin aut Yufu Zang verfasserin aut He Zheng verfasserin aut Wei Jiang verfasserin aut Mianqing Zhong verfasserin aut Fei Ma verfasserin aut In ISPRS International Journal of Geo-Information MDPI AG, 2012 10(2021), 7, p 444 (DE-627)689130961 (DE-600)2655790-3 22209964 nnns volume:10 year:2021 number:7, p 444 https://doi.org/10.3390/ijgi10070444 kostenfrei https://doaj.org/article/d7f43e67cd454dbe80fb811e09f09407 kostenfrei https://www.mdpi.com/2220-9964/10/7/444 kostenfrei https://doaj.org/toc/2220-9964 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 10 2021 7, p 444
allfieldsGer	10.3390/ijgi10070444 doi (DE-627)DOAJ071768564 (DE-599)DOAJd7f43e67cd454dbe80fb811e09f09407 DE-627 ger DE-627 rakwb eng G1-922 Jianfeng Zhu verfasserin aut Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be directly applied to point cloud classification because of the disordered and unstructured characteristics of point clouds. In this paper, we design a novel convolution operator to extract local features directly from unstructured points. Based on this convolution operator, we define the convolution layer, construct a convolution neural network to learn multi-level features from the point cloud, and obtain the category label of each point in an end-to-end manner. The proposed method is evaluated on two ALS datasets: the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 3D Labeling benchmark and the 2019 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest (DFC) 3D dataset. The results show that our method achieves state-of-the-art performance for ALS point cloud classification, especially for the larger dataset DFC: we get an overall accuracy of 97.74% and a mean intersection over union (mIoU) of 0.9202, ranking in first place on the contest website. point cloud classification semantic segmentation airborne laser scanning convolutional neural network deep learning Geography (General) Lichun Sui verfasserin aut Yufu Zang verfasserin aut He Zheng verfasserin aut Wei Jiang verfasserin aut Mianqing Zhong verfasserin aut Fei Ma verfasserin aut In ISPRS International Journal of Geo-Information MDPI AG, 2012 10(2021), 7, p 444 (DE-627)689130961 (DE-600)2655790-3 22209964 nnns volume:10 year:2021 number:7, p 444 https://doi.org/10.3390/ijgi10070444 kostenfrei https://doaj.org/article/d7f43e67cd454dbe80fb811e09f09407 kostenfrei https://www.mdpi.com/2220-9964/10/7/444 kostenfrei https://doaj.org/toc/2220-9964 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 10 2021 7, p 444
allfieldsSound	10.3390/ijgi10070444 doi (DE-627)DOAJ071768564 (DE-599)DOAJd7f43e67cd454dbe80fb811e09f09407 DE-627 ger DE-627 rakwb eng G1-922 Jianfeng Zhu verfasserin aut Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be directly applied to point cloud classification because of the disordered and unstructured characteristics of point clouds. In this paper, we design a novel convolution operator to extract local features directly from unstructured points. Based on this convolution operator, we define the convolution layer, construct a convolution neural network to learn multi-level features from the point cloud, and obtain the category label of each point in an end-to-end manner. The proposed method is evaluated on two ALS datasets: the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 3D Labeling benchmark and the 2019 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest (DFC) 3D dataset. The results show that our method achieves state-of-the-art performance for ALS point cloud classification, especially for the larger dataset DFC: we get an overall accuracy of 97.74% and a mean intersection over union (mIoU) of 0.9202, ranking in first place on the contest website. point cloud classification semantic segmentation airborne laser scanning convolutional neural network deep learning Geography (General) Lichun Sui verfasserin aut Yufu Zang verfasserin aut He Zheng verfasserin aut Wei Jiang verfasserin aut Mianqing Zhong verfasserin aut Fei Ma verfasserin aut In ISPRS International Journal of Geo-Information MDPI AG, 2012 10(2021), 7, p 444 (DE-627)689130961 (DE-600)2655790-3 22209964 nnns volume:10 year:2021 number:7, p 444 https://doi.org/10.3390/ijgi10070444 kostenfrei https://doaj.org/article/d7f43e67cd454dbe80fb811e09f09407 kostenfrei https://www.mdpi.com/2220-9964/10/7/444 kostenfrei https://doaj.org/toc/2220-9964 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 10 2021 7, p 444
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callnumber-first	G - Geography, Anthropology, Recreation
author	Jianfeng Zhu
spellingShingle	Jianfeng Zhu misc G1-922 misc point cloud classification misc semantic segmentation misc airborne laser scanning misc convolutional neural network misc deep learning misc Geography (General) Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network
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topic_title	G1-922 Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network point cloud classification semantic segmentation airborne laser scanning convolutional neural network deep learning
topic	misc G1-922 misc point cloud classification misc semantic segmentation misc airborne laser scanning misc convolutional neural network misc deep learning misc Geography (General)
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title	Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network
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title_full	Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network
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title_sort	classification of airborne laser scanning point cloud using point-based convolutional neural network
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title_auth	Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network
abstract	In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be directly applied to point cloud classification because of the disordered and unstructured characteristics of point clouds. In this paper, we design a novel convolution operator to extract local features directly from unstructured points. Based on this convolution operator, we define the convolution layer, construct a convolution neural network to learn multi-level features from the point cloud, and obtain the category label of each point in an end-to-end manner. The proposed method is evaluated on two ALS datasets: the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 3D Labeling benchmark and the 2019 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest (DFC) 3D dataset. The results show that our method achieves state-of-the-art performance for ALS point cloud classification, especially for the larger dataset DFC: we get an overall accuracy of 97.74% and a mean intersection over union (mIoU) of 0.9202, ranking in first place on the contest website.
abstractGer	In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be directly applied to point cloud classification because of the disordered and unstructured characteristics of point clouds. In this paper, we design a novel convolution operator to extract local features directly from unstructured points. Based on this convolution operator, we define the convolution layer, construct a convolution neural network to learn multi-level features from the point cloud, and obtain the category label of each point in an end-to-end manner. The proposed method is evaluated on two ALS datasets: the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 3D Labeling benchmark and the 2019 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest (DFC) 3D dataset. The results show that our method achieves state-of-the-art performance for ALS point cloud classification, especially for the larger dataset DFC: we get an overall accuracy of 97.74% and a mean intersection over union (mIoU) of 0.9202, ranking in first place on the contest website.
abstract_unstemmed	In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be directly applied to point cloud classification because of the disordered and unstructured characteristics of point clouds. In this paper, we design a novel convolution operator to extract local features directly from unstructured points. Based on this convolution operator, we define the convolution layer, construct a convolution neural network to learn multi-level features from the point cloud, and obtain the category label of each point in an end-to-end manner. The proposed method is evaluated on two ALS datasets: the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 3D Labeling benchmark and the 2019 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest (DFC) 3D dataset. The results show that our method achieves state-of-the-art performance for ALS point cloud classification, especially for the larger dataset DFC: we get an overall accuracy of 97.74% and a mean intersection over union (mIoU) of 0.9202, ranking in first place on the contest website.
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title_short	Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network
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author2	Lichun Sui Yufu Zang He Zheng Wei Jiang Mianqing Zhong Fei Ma
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up_date	2024-07-03T22:05:33.720Z
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