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Sensors 2016, 16(7), 1110; doi:10.3390/s16071110

A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery

1
Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
2
Faculty of Science and Technology, Federation University Australia, Churchill VIC 3842, Australia
3
School of Information and Communication Technology, Griffith University, Nathan QLD 4111, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Radislav A. Potyrailo
Received: 20 April 2016 / Revised: 6 July 2016 / Accepted: 14 July 2016 / Published: 19 July 2016
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [8350 KB, uploaded 19 July 2016]   |  

Abstract

Existing automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics. View Full-Text
Keywords: building extraction; LiDAR; photogrammetric imagery; transparent roof building; dense vegetation; small size building building extraction; LiDAR; photogrammetric imagery; transparent roof building; dense vegetation; small size building
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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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MDPI and ACS Style

Siddiqui, F.U.; Teng, S.W.; Awrangjeb, M.; Lu, G. A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery. Sensors 2016, 16, 1110.

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