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

verfasst von
S. Aghayari, A. Hadavand, S. Mohamadnezhad Niazi, Mohammad Omidalizarandi
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).

Organisationseinheit(en)
Geodätisches Institut
Externe Organisation(en)
Ideh Pardazan Tosseah Consulting Engineering Company
Typ
Konferenzaufsatz in Fachzeitschrift
Journal
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Band
X-4/W1-2022
Seiten
9-17
ISSN
2194-9042
Publikationsdatum
13.01.2023
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
Elektronische Version(en)
https://doi.org/10.5194/isprs-annals-X-4-W1-2022-9-2023 (Zugang: Offen)