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A shadow–overlapping algorithm for estimating building heights from VHR satellite images

Kadhim, Nada and Mourshed, Monjur 2018. A shadow–overlapping algorithm for estimating building heights from VHR satellite images. IEEE Geoscience and Remote Sensing Letters 15 (1) , pp. 8-12. 10.1109/LGRS.2017.2762424

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Abstract

Building height is a key geometric attribute for generating 3D building models. We propose a novel four-stage approach for automated estimation of building heights from their shadows in very-high-resolution (VHR) multispectral images. First, a building’s actual shadow regions are detected by applying ratio-band algorithm to the VHR image. Second, 2D building footprint geometries are identified using graph theory and morphological fuzzy processing techniques. Third, artificial shadow regions are simulated using the identified building footprint and solar information in the image metadata at pre-defined height increments. Finally, the difference between the actual and simulated shadow regions at every height increment is computed using Jaccard similarity coefficient. The estimated building height corresponds to the height of the simulated shadow region that resulted in the maximum value for Jaccard index. The algorithm is tested on seven urban sites in Cardiff, UK with various levels of morphological complexity. Our method outperforms the past attempts, and mean error is reduced by at least 21%.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: G Geography. Anthropology. Recreation > G Geography (General)
G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography
G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science > Q Science (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TH Building construction
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1545-598X
Funders: HCED and Cardiff University
Date of First Compliant Deposit: 26 October 2017
Date of Acceptance: 9 October 2017
Last Modified: 27 Jul 2019 22:17
URI: http://orca.cf.ac.uk/id/eprint/105351

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