We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Semantic Segmentation with High-Resolution Sentinel-1 SAR Data.
- Authors
Erten, Hakan; Bostanci, Erkan; Acici, Koray; Guzel, Mehmet Serdar; Asuroglu, Tunc; Aydin, Ayhan
- Abstract
The world's high-resolution images are supplied by a radar system named Synthetic Aperture Radar (SAR). Semantic SAR image segmentation proposes a computer-based solution to make segmentation tasks easier. When conducting scientific research, accessing freely available datasets and images with low noise levels is rare. However, SAR images can be accessed for free. We propose a novel process for labeling Sentinel-1 SAR radar images, which the European Space Agency (ESA) provides free of charge. This process involves denoising the images and using an automatically created dataset with pioneering deep neural networks to augment the results of the semantic segmentation task. In order to exhibit the power of our denoising process, we match the results of our newly created dataset with speckled noise and noise-free versions. Thus, we attained a mean intersection over union (mIoU) of 70.60% and overall pixel accuracy (PA) of 92.23 with the HRNet model. These deep learning segmentation methods were also assessed with the McNemar test. Our experiments on the newly created Sentinel-1 dataset establish that combining our pipeline with deep neural networks results in recognizable improvements in challenging semantic segmentation accuracy and mIoU values.
- Subjects
EUROPEAN Space Agency; ARTIFICIAL neural networks; SYNTHETIC aperture radar; DEEP learning; IMAGE segmentation; IMAGE denoising; VALUES (Ethics)
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 10, p6025
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app13106025