| BUILDING DETECTION FROM AERIAL IMAGERY USING INCEPTION RESNET UNET AND UNET ARCHITECTURES |
| Paper ID : 1213-GEOSPATIAL (R3) |
| Authors: |
|
Saleh Aghayari *1, Ahmad Hadavand1, saeed mohamadnezhad Niazi1, Mohammad Omidalizarandi2 1Photgrammetry and computer vision department, Ideh Pardazan Tossee co, Tehran, Iran 2Geodetic Institute, Leibniz University Hannover, Germany |
| Abstract: |
| Buildings are one of the key components in change detection, urban planning, and monitoring. The automatic extraction of the building boundaries 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 boundaries, 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). |
| Keywords: |
| Large-scale monitoring, Building detection, Image segmentation, Residual blocks, Skip connection. |
| Status : Paper Accepted (Oral Presentation) |
