Details
| Original language | English |
|---|---|
| Pages (from-to) | 9-17 |
| Number of pages | 9 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 10 |
| Issue number | 4/W1-2022 |
| Publication status | Published - 13 Jan 2023 |
| Event | 6th SMPR and 4th GIResearch, ISPRS Geospatial Conference - Duration: 19 Feb 2023 → 22 Feb 2023 |
Abstract
Keywords
- Large-scale monitoring, Building detection, Image segmentation, Residual blocks, Skip connection
ASJC Scopus subject areas
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Physics and Astronomy(all)
- Instrumentation
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 10, No. 4/W1-2022, 13.01.2023, p. 9-17.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models
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).
AB - 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).
KW - Large-scale monitoring
KW - Building detection
KW - Image segmentation
KW - Residual blocks
KW - Skip connection
UR - http://www.scopus.com/inward/record.url?scp=85146937067&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-X-4-W1-2022-9-2023
DO - 10.5194/isprs-annals-X-4-W1-2022-9-2023
M3 - Conference article
VL - 10
SP - 9
EP - 17
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 4/W1-2022
T2 - 6th SMPR and 4th GIResearch, ISPRS Geospatial Conference
Y2 - 19 February 2023 through 22 February 2023
ER -