Abstract
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%.
Highlights
We proposed and evaluated an unsupervised and automatic building extraction method dedicated to a large-scale urban scene
A preliminary extraction of building boundaries from the Light Detection and Ranging (LiDAR) point cloud is carried out. These boundaries are used as initial points for the Super-Resolution-based Snake Model (SRSM) as well as in the improved balloon force
In order to resolve the sparsity problem related to the LiDAR data spatial resolution compared to an optical imagery dataset [21], we propose a super-resolution process
Summary
This research work presents an effective solution for extracting buildings from urban and residential environments in a large scale. Such a task plays an important role in the context of flood risk anticipation, which is asserted with a particular importance in the province of Quebec, Canada [6]. Such a context requires accurate and regularly updated building footprint location and boundary, which enable the extraction of further essential structural and occupational characteristics of buildings (e.g., first floor, basement openings). The scalability of this solution—i.e., the ability to maintain its effectiveness when expanding from a local area to a large area [7]—is crucially important considering the scale of the study (i.e., at the scale of the province of Quebec)
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