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Article

Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation

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Doctoral School of Earth Sciences, Department of Physical Geography and Geoinformation Systems, Faculty of Sciences and Technology, University of Debrecen, 4032 Debrecen, Hungary
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Department of Physical Geography and Geoinformation Systems, Faculty of Sciences and Technology, University of Debrecen, 4032 Debrecen, Hungary
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Department of Civil Engineering, Faculty of Engineering, University of Debrecen, 4028 Debrecen, Hungary
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Envirosense Ltd., 4032 Debrecen, Hungary
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(15), 2397; https://doi.org/10.3390/rs12152397
Received: 31 May 2020 / Revised: 15 July 2020 / Accepted: 24 July 2020 / Published: 26 July 2020
(This article belongs to the Special Issue Machine and Deep Learning for Earth Observation Data Analysis)
Urban sprawl related increase of built-in areas requires reliable monitoring methods and remote sensing can be an efficient technique. Aerial surveys, with high spatial resolution, provide detailed data for building monitoring, but archive images usually have only visible bands. We aimed to reveal the efficiency of visible orthophotographs and photogrammetric dense point clouds in building detection with segmentation-based machine learning (with five algorithms) using visible bands, texture information, and spectral and morphometric indices in different variable sets. Usually random forest (RF) had the best (99.8%) and partial least squares the worst overall accuracy (~60%). We found that >95% accuracy can be gained even in class level. Recursive feature elimination (RFE) was an efficient variable selection tool, its result with six variables was like when we applied all the available 31 variables. Morphometric indices had 82% producer’s and 85% user’s Accuracy (PA and UA, respectively) and combining them with spectral and texture indices, it had the largest contribution in the improvement. However, morphometric indices are not always available but by adding texture and spectral indices to red-green-blue (RGB) bands the PA improved with 12% and the UA with 6%. Building extraction from visual aerial surveys can be accurate, and archive images can be involved in the time series of a monitoring. View Full-Text
Keywords: photogrammetry; RGB indices; image texture; morphometric indices; recursive feature elimination; random forest; support vector machine; multiple adaptive regression spline; partial least squares photogrammetry; RGB indices; image texture; morphometric indices; recursive feature elimination; random forest; support vector machine; multiple adaptive regression spline; partial least squares
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MDPI and ACS Style

Schlosser, A.D.; Szabó, G.; Bertalan, L.; Varga, Z.; Enyedi, P.; Szabó, S. Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation. Remote Sens. 2020, 12, 2397. https://doi.org/10.3390/rs12152397

AMA Style

Schlosser AD, Szabó G, Bertalan L, Varga Z, Enyedi P, Szabó S. Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation. Remote Sensing. 2020; 12(15):2397. https://doi.org/10.3390/rs12152397

Chicago/Turabian Style

Schlosser, Aletta D., Gergely Szabó, László Bertalan, Zsolt Varga, Péter Enyedi, and Szilárd Szabó. 2020. "Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation" Remote Sensing 12, no. 15: 2397. https://doi.org/10.3390/rs12152397

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