Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models

Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review

Autorschaft

  • S. Aghayari
  • A. Hadavand
  • S. Mohamadnezhad Niazi
  • Mohammad Omidalizarandi

Organisationseinheiten

Externe Organisationen

  • Ideh Pardazan Tosseah Consulting Engineering Company
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)9-17
Seitenumfang9
FachzeitschriftISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Jahrgang10
Ausgabenummer4/W1-2022
PublikationsstatusVeröffentlicht - 13 Jan. 2023
Veranstaltung6th SMPR and 4th GIResearch, ISPRS Geospatial Conference -
Dauer: 19 Feb. 2023 → 22 Feb. 2023

Abstract

Buildings are one of the key components in change detection, urban planning, and monitoring. The automatic extraction of the building from high-resolution aerial imagery is still challenging due to the variations in their shapes, structures, textures, and colours. Recently, the convolutional neural networks (CNN) show a significant improvement in object detection and extraction that surpasses other methods. To extract building, in this paper two segmentation architectures, the UNet and the Inception ResNet UNet are implemented and then tested on the Inria aerial image datasets. The Inception ResNet UNet utilizes the Inception architecture and residual blocks. This makes the model wide and deep, though there are a few differences between numbers of UNet and Inception ResNet UNet parameters. The analyses show that UNet has a high rate of metrics in the training progress. However, on the unseen dataset, Inception ResNet UNet extracts buildings more accurately (97.95% accuracy and 0.96 in the dice metric) in comparison with UNet (94.30% accuracy and 0.55 in the dice metric).

ASJC Scopus Sachgebiete

Zitieren

Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models. / Aghayari, S.; Hadavand, A.; Mohamadnezhad Niazi, S. et al.
in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jahrgang 10, Nr. 4/W1-2022, 13.01.2023, S. 9-17.

Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review

Aghayari, S, Hadavand, A, Mohamadnezhad Niazi, S & Omidalizarandi, M 2023, 'Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models', ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jg. 10, Nr. 4/W1-2022, S. 9-17. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-9-2023
Aghayari, S., Hadavand, A., Mohamadnezhad Niazi, S., & Omidalizarandi, M. (2023). Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10(4/W1-2022), 9-17. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-9-2023
Aghayari S, Hadavand A, Mohamadnezhad Niazi S, Omidalizarandi M. Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2023 Jan 13;10(4/W1-2022):9-17. doi: 10.5194/isprs-annals-X-4-W1-2022-9-2023
Aghayari, S. ; Hadavand, A. ; Mohamadnezhad Niazi, S. et al. / Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models. in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2023 ; Jahrgang 10, Nr. 4/W1-2022. S. 9-17.
Download
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abstract = "Buildings are one of the key components in change detection, urban planning, and monitoring. The automatic extraction of the building from high-resolution aerial imagery is still challenging due to the variations in their shapes, structures, textures, and colours. Recently, the convolutional neural networks (CNN) show a significant improvement in object detection and extraction that surpasses other methods. To extract building, in this paper two segmentation architectures, the UNet and the Inception ResNet UNet are implemented and then tested on the Inria aerial image datasets. The Inception ResNet UNet utilizes the Inception architecture and residual blocks. This makes the model wide and deep, though there are a few differences between numbers of UNet and Inception ResNet UNet parameters. The analyses show that UNet has a high rate of metrics in the training progress. However, on the unseen dataset, Inception ResNet UNet extracts buildings more accurately (97.95% accuracy and 0.96 in the dice metric) in comparison with UNet (94.30% accuracy and 0.55 in the dice metric).",
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note = "Funding Information: This research was supported by Ideh Pardazan Tosseah Consulting Engineering Company. We are grateful to all of those with whom we have had the pleasure to work during this and other related projects.; 6th SMPR and 4th GIResearch, ISPRS Geospatial Conference ; Conference date: 19-02-2023 Through 22-02-2023",
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AU - Aghayari, S.

AU - Hadavand, A.

AU - Mohamadnezhad Niazi, S.

AU - Omidalizarandi, Mohammad

N1 - Funding Information: This research was supported by Ideh Pardazan Tosseah Consulting Engineering Company. We are grateful to all of those with whom we have had the pleasure to work during this and other related projects.

PY - 2023/1/13

Y1 - 2023/1/13

N2 - Buildings are one of the key components in change detection, urban planning, and monitoring. The automatic extraction of the building from high-resolution aerial imagery is still challenging due to the variations in their shapes, structures, textures, and colours. Recently, the convolutional neural networks (CNN) show a significant improvement in object detection and extraction that surpasses other methods. To extract building, in this paper two segmentation architectures, the UNet and the Inception ResNet UNet are implemented and then tested on the Inria aerial image datasets. The Inception ResNet UNet utilizes the Inception architecture and residual blocks. This makes the model wide and deep, though there are a few differences between numbers of UNet and Inception ResNet UNet parameters. The analyses show that UNet has a high rate of metrics in the training progress. However, on the unseen dataset, Inception ResNet UNet extracts buildings more accurately (97.95% accuracy and 0.96 in the dice metric) in comparison with UNet (94.30% accuracy and 0.55 in the dice metric).

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KW - Building detection

KW - Image segmentation

KW - Residual blocks

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