Fully Convolutional Networks for Ground Classification from Airborne Laser Scanner data

Aldino Rizaldy; C. Persello; C.M. Gevaert; S.J. Oude Elberink; G. Vosselman

Fully Convolutional Networks for Ground Classification from Airborne Laser Scanner data

Aldino Rizaldy, C. Persello, C.M. Gevaert, S.J. Oude Elberink, G. Vosselman

Research output: Contribution to conference › Abstract › Other research output

Abstract

We present an efficient procedure to classify airborne laser scanner data into ground and non-ground. The classification is performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on AHN-3 data resulting in 4.02% of total error, 2.15% of type I error and 6.14% of type II error. We show that this method can be extended to further classify the point cloud into buildings and vegetation.

Original language	English
Pages	5
Number of pages	1
Publication status	Published - 28 Nov 2018
Event	NCG symposium 2018 - Wageningen university, Wageningen, Netherlands Duration: 29 Nov 2018 → 29 Nov 2018 https://ncgeo.nl/index.php/nl/actueel/nieuws/item/2781-programma-ncg-symposium-2018

Conference

Conference	NCG symposium 2018
Country/Territory	Netherlands
City	Wageningen
Period	29/11/18 → 29/11/18
Internet address	https://ncgeo.nl/index.php/nl/actueel/nieuws/item/2781-programma-ncg-symposium-2018

Cite this

@conference{d1a8ff2fede642118bc99af67efb0aa2,

title = "Fully Convolutional Networks for Ground Classification from Airborne Laser Scanner data",

abstract = "We present an efficient procedure to classify airborne laser scanner data into ground and non-ground. The classification is performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on AHN-3 data resulting in 4.02% of total error, 2.15% of type I error and 6.14% of type II error. We show that this method can be extended to further classify the point cloud into buildings and vegetation.",

author = "Aldino Rizaldy and C. Persello and C.M. Gevaert and {Oude Elberink}, S.J. and G. Vosselman",

year = "2018",

month = nov,

day = "28",

language = "English",

pages = "5",

note = "NCG symposium 2018 ; Conference date: 29-11-2018 Through 29-11-2018",

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TY - CONF

T1 - Fully Convolutional Networks for Ground Classification from Airborne Laser Scanner data

AU - Rizaldy, Aldino

AU - Persello, C.

AU - Gevaert, C.M.

AU - Oude Elberink, S.J.

AU - Vosselman, G.

PY - 2018/11/28

Y1 - 2018/11/28

N2 - We present an efficient procedure to classify airborne laser scanner data into ground and non-ground. The classification is performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on AHN-3 data resulting in 4.02% of total error, 2.15% of type I error and 6.14% of type II error. We show that this method can be extended to further classify the point cloud into buildings and vegetation.

AB - We present an efficient procedure to classify airborne laser scanner data into ground and non-ground. The classification is performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on AHN-3 data resulting in 4.02% of total error, 2.15% of type I error and 6.14% of type II error. We show that this method can be extended to further classify the point cloud into buildings and vegetation.

UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2018/pres/persello_ful_abs.pdf

M3 - Abstract

SP - 5

T2 - NCG symposium 2018

Y2 - 29 November 2018 through 29 November 2018

ER -

Fully Convolutional Networks for Ground Classification from Airborne Laser Scanner data

Abstract

Conference

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