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Open AccessArticle

Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image

1
IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238 Brest, France
2
Department of Geomatics, Université Laval, Quebec City, QC G1V 0A6, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1702; https://doi.org/10.3390/rs12111702
Received: 18 April 2020 / Revised: 20 May 2020 / Accepted: 22 May 2020 / Published: 26 May 2020
(This article belongs to the Special Issue 3D City Modelling and Change Detection Using Remote Sensing Data)
Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such a challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution Light Detection and Ranging (LiDAR)-based elevation images—called z-images—generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%. View Full-Text
Keywords: building extraction; building footprint extraction; airborne LiDAR; optical imagery; active contour model; snake model; super-resolution; unsupervised approach; large scale building extraction; building footprint extraction; airborne LiDAR; optical imagery; active contour model; snake model; super-resolution; unsupervised approach; large scale
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MDPI and ACS Style

Nguyen, T.H.; Daniel, S.; Guériot, D.; Sintès, C.; Le Caillec, J.-M. Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image. Remote Sens. 2020, 12, 1702.

AMA Style

Nguyen TH, Daniel S, Guériot D, Sintès C, Le Caillec J-M. Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image. Remote Sensing. 2020; 12(11):1702.

Chicago/Turabian Style

Nguyen, Thanh H.; Daniel, Sylvie; Guériot, Didier; Sintès, Christophe; Le Caillec, Jean-Marc. 2020. "Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image" Remote Sens. 12, no. 11: 1702.

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