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Article

Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data

1
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT, University of Technology Sydney, Sydney, NSW 2007, Australia
2
Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
3
Department of Geology and Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editors: Angelica I. Aviles-Rivero, Weijia Li, Lichao Mou, Runmin Dong and Juepeng Zheng
Remote Sens. 2021, 13(18), 3710; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183710
Received: 15 August 2021 / Revised: 14 September 2021 / Accepted: 15 September 2021 / Published: 16 September 2021
(This article belongs to the Special Issue Deep Learning in Remote Sensing Application)
Terrestrial features extraction, such as roads and buildings from aerial images using an automatic system, has many usages in an extensive range of fields, including disaster management, change detection, land cover assessment, and urban planning. This task is commonly tough because of complex scenes, such as urban scenes, where buildings and road objects are surrounded by shadows, vehicles, trees, etc., which appear in heterogeneous forms with lower inter-class and higher intra-class contrasts. Moreover, such extraction is time-consuming and expensive to perform by human specialists manually. Deep convolutional models have displayed considerable performance for feature segmentation from remote sensing data in the recent years. However, for the large and continuous area of obstructions, most of these techniques still cannot detect road and building well. Hence, this work’s principal goal is to introduce two novel deep convolutional models based on UNet family for multi-object segmentation, such as roads and buildings from aerial imagery. We focused on buildings and road networks because these objects constitute a huge part of the urban areas. The presented models are called multi-level context gating UNet (MCG-UNet) and bi-directional ConvLSTM UNet model (BCL-UNet). The proposed methods have the same advantages as the UNet model, the mechanism of densely connected convolutions, bi-directional ConvLSTM, and squeeze and excitation module to produce the segmentation maps with a high resolution and maintain the boundary information even under complicated backgrounds. Additionally, we implemented a basic efficient loss function called boundary-aware loss (BAL) that allowed a network to concentrate on hard semantic segmentation regions, such as overlapping areas, small objects, sophisticated objects, and boundaries of objects, and produce high-quality segmentation maps. The presented networks were tested on the Massachusetts building and road datasets. The MCG-UNet improved the average F1 accuracy by 1.85%, and 1.19% and 6.67% and 5.11% compared with UNet and BCL-UNet for road and building extraction, respectively. Additionally, the presented MCG-UNet and BCL-UNet networks were compared with other state-of-the-art deep learning-based networks, and the results proved the superiority of the networks in multi-object segmentation tasks. View Full-Text
Keywords: building extraction; boundary-aware loss; deep learning; remote sensing; road extraction building extraction; boundary-aware loss; deep learning; remote sensing; road extraction
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MDPI and ACS Style

Abdollahi, A.; Pradhan, B.; Shukla, N.; Chakraborty, S.; Alamri, A. Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data. Remote Sens. 2021, 13, 3710. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183710

AMA Style

Abdollahi A, Pradhan B, Shukla N, Chakraborty S, Alamri A. Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data. Remote Sensing. 2021; 13(18):3710. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183710

Chicago/Turabian Style

Abdollahi, Abolfazl, Biswajeet Pradhan, Nagesh Shukla, Subrata Chakraborty, and Abdullah Alamri. 2021. "Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data" Remote Sensing 13, no. 18: 3710. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183710

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