Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery

Date

2013-03-01

Author

Ok, Ali Ozgun
Senaras, Caglar
Yuksel, Baris

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper introduces a new approach for the automated detection of buildings from monocular very high resolution (VHR) optical satellite images. First, we investigate the shadow evidence to focus on building regions. To do that, we propose a new fuzzy landscape generation approach to model the directional spatial relationship between buildings and their shadows. Once all landscapes are collected, a pruning process is developed to eliminate the landscapes that may occur due to non-building objects. The final building regions are detected by GrabCut partitioning approach. In this paper, the input requirements of the GrabCut partitioning are automatically extracted from the previously determined shadow and landscape regions, so that the approach gained an efficient fully automated behavior for the detection of buildings. Extensive experiments performed on 20 test sites selected from a set of QuickBird and Geoeye-1 VHR images showed that the proposed approach accurately detects buildings with arbitrary shapes and sizes in complex environments. The tests also revealed that even under challenging environmental and illumination conditions, reasonable building detection performances could be achieved by the proposed approach.

Subject Keywords

Very high resolution (VHR) satellite imagery, GrabCut partitioning, Fuzzy landscape generation, Building detection

URI

https://hdl.handle.net/11511/66485

Journal

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

DOI

https://doi.org/10.1109/tgrs.2012.2207123

Collections

Graduate School of Informatics, Article

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Citation Formats

A. O. Ok, C. Senaras, and B. Yuksel, “Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery,” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, pp. 1701–1717, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66485.