Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classification
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...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI
2020
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/38105/1/38105.pdf |
| _version_ | 1825949161142353920 |
|---|---|
| author | Gibril, Mohamed Barakat A. Kalantar, Bahareh Al-Ruzouq, Rami Ueda, Naonori Saeidi, Vahideh Shanableh, Abdallah Mansor, Shattri Mohd Shafri, Helmi Zulhaidi |
| author_facet | Gibril, Mohamed Barakat A. Kalantar, Bahareh Al-Ruzouq, Rami Ueda, Naonori Saeidi, Vahideh Shanableh, Abdallah Mansor, Shattri Mohd Shafri, Helmi Zulhaidi |
| author_sort | Gibril, Mohamed Barakat A. |
| collection | UPM |
| description | 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. |
| first_indexed | 2024-03-06T08:40:18Z |
| format | Article |
| id | upm.eprints-38105 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:40:18Z |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | upm.eprints-381052020-04-14T13:58:44Z http://psasir.upm.edu.my/id/eprint/38105/ Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classification Gibril, Mohamed Barakat A. Kalantar, Bahareh Al-Ruzouq, Rami Ueda, Naonori Saeidi, Vahideh Shanableh, Abdallah Mansor, Shattri Mohd Shafri, Helmi Zulhaidi 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. MDPI 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/38105/1/38105.pdf Gibril, Mohamed Barakat A. and Kalantar, Bahareh and Al-Ruzouq, Rami and Ueda, Naonori and Saeidi, Vahideh and Shanableh, Abdallah and Mansor, Shattri and Mohd Shafri, Helmi Zulhaidi (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 Sensing, 12 (7). art. no. 1081. pp. 1-27. ISSN 2072-4292 https://www.mdpi.com/2072-4292/12/7/1081 10.3390/rs12071081 |
| spellingShingle | Gibril, Mohamed Barakat A. Kalantar, Bahareh Al-Ruzouq, Rami Ueda, Naonori Saeidi, Vahideh Shanableh, Abdallah Mansor, Shattri Mohd Shafri, Helmi Zulhaidi Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classification |
| title | Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classification |
| title_full | Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classification |
| title_fullStr | Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classification |
| title_full_unstemmed | Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classification |
| title_short | Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classification |
| title_sort | mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle based images using adaptive multiscale image segmentation and classification |
| url | http://psasir.upm.edu.my/id/eprint/38105/1/38105.pdf |
| work_keys_str_mv | AT gibrilmohamedbarakata mappingheterogeneousurbanlandscapesfromthefusionofdigitalsurfacemodelandunmannedaerialvehiclebasedimagesusingadaptivemultiscaleimagesegmentationandclassification AT kalantarbahareh mappingheterogeneousurbanlandscapesfromthefusionofdigitalsurfacemodelandunmannedaerialvehiclebasedimagesusingadaptivemultiscaleimagesegmentationandclassification AT alruzouqrami mappingheterogeneousurbanlandscapesfromthefusionofdigitalsurfacemodelandunmannedaerialvehiclebasedimagesusingadaptivemultiscaleimagesegmentationandclassification AT uedanaonori mappingheterogeneousurbanlandscapesfromthefusionofdigitalsurfacemodelandunmannedaerialvehiclebasedimagesusingadaptivemultiscaleimagesegmentationandclassification AT saeidivahideh mappingheterogeneousurbanlandscapesfromthefusionofdigitalsurfacemodelandunmannedaerialvehiclebasedimagesusingadaptivemultiscaleimagesegmentationandclassification AT shanablehabdallah mappingheterogeneousurbanlandscapesfromthefusionofdigitalsurfacemodelandunmannedaerialvehiclebasedimagesusingadaptivemultiscaleimagesegmentationandclassification AT mansorshattri mappingheterogeneousurbanlandscapesfromthefusionofdigitalsurfacemodelandunmannedaerialvehiclebasedimagesusingadaptivemultiscaleimagesegmentationandclassification AT mohdshafrihelmizulhaidi mappingheterogeneousurbanlandscapesfromthefusionofdigitalsurfacemodelandunmannedaerialvehiclebasedimagesusingadaptivemultiscaleimagesegmentationandclassification |