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Open AccessArticle

Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification

1
GIS & Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, 27272 Sharjah, UAE
2
Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400 Selangor, Malaysia
3
RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan
4
Department of Civil and Environmental Engineering, University of Sharjah, 27272 Sharjah, UAE
5
Department of Mapping and Surveying, Darya Tarsim Consulting Engineers Co. Ltd., 1457843993 Tehran, Iran
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1081; https://doi.org/10.3390/rs12071081
Received: 3 March 2020 / Revised: 23 March 2020 / Accepted: 24 March 2020 / Published: 27 March 2020
(This article belongs to the Special Issue UAV Photogrammetry and Remote Sensing)
Considering the high-level details in an ultrahigh-spatial-resolution (UHSR) unmanned aerial vehicle (UAV) dataset, detailed mapping of heterogeneous urban landscapes is extremely challenging because of the spectral similarity between classes. In this study, adaptive hierarchical image segmentation optimization, multilevel feature selection, and multiscale (MS) supervised machine learning (ML) models were integrated to accurately generate detailed maps for heterogeneous urban areas from the fusion of the UHSR orthomosaic and digital surface model (DSM). The integrated approach commenced through a preliminary MS image segmentation parameter selection, followed by the application of three supervised ML models, namely, random forest (RF), support vector machine (SVM), and decision tree (DT). These models were implemented at the optimal MS levels to identify preliminary information, such as the optimal segmentation level(s) and relevant features, for extracting 12 land use/land cover (LULC) urban classes from the fused datasets. Using the information obtained from the first phase of the analysis, detailed MS classification was iteratively conducted to improve the classification accuracy and derive the final urban LULC maps. Two UAV-based datasets were used to develop and assess the effectiveness of the proposed framework. The hierarchical classification of the pilot study area showed that the RF was superior with an overall accuracy (OA) of 94.40% and a kappa coefficient (K) of 0.938, followed by SVM (OA = 92.50% and K = 0.917) and DT (OA = 91.60% and K = 0.908). The classification results of the second dataset revealed that SVM was superior with an OA of 94.45% and K of 0.938, followed by RF (OA = 92.46% and K = 0.916) and DT (OA = 90.46% and K = 0.893). The proposed framework exhibited an excellent potential for the detailed mapping of heterogeneous urban landscapes from the fusion of UHSR orthophoto and DSM images using various ML models. View Full-Text
Keywords: unmanned aerial vehicle; urban LULC; GEOBIA; multiscale classification unmanned aerial vehicle; urban LULC; GEOBIA; multiscale classification
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MDPI and ACS Style

Gibril, M.B.A.; Kalantar, B.; Al-Ruzouq, R.; Ueda, N.; Saeidi, V.; Shanableh, A.; Mansor, S.; Shafri, H.Z.M. Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification. Remote Sens. 2020, 12, 1081. https://doi.org/10.3390/rs12071081

AMA Style

Gibril MBA, Kalantar B, Al-Ruzouq R, Ueda N, Saeidi V, Shanableh A, Mansor S, Shafri HZM. Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification. Remote Sensing. 2020; 12(7):1081. https://doi.org/10.3390/rs12071081

Chicago/Turabian Style

Gibril, Mohamed B.A.; Kalantar, Bahareh; Al-Ruzouq, Rami; Ueda, Naonori; Saeidi, Vahideh; Shanableh, Abdallah; Mansor, Shattri; Shafri, Helmi Z.M. 2020. "Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification" Remote Sens. 12, no. 7: 1081. https://doi.org/10.3390/rs12071081

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