Deep Learning Powered Non-Local Speckle Filtering of Sentinel-1 Imagery and its Potential for Humanitarian Action
- Resource Type
- Conference
- Authors
- Jaggy, Niklas; Gella, Getachew Workineh; Dabiri, Zahra; Lang, Stefan; Wendt, Lorenz; Braun, Andreas
- Source
- IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2023 - 2023 IEEE International. :818-821 Jul, 2023
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Geoscience
Signal Processing and Analysis
Optical filters
Satellite constellations
Filtering
European Space Agency
Sociology
Speckle
Task analysis
Deep Learning
Humanitarian Action
SAR
Sentinel-1
- Language
- ISSN
- 2153-7003
The continuously increasing amount of Synthetic Aperture Radar (SAR) data is becoming an important complementary source of information for humanitarian action especially when the availability of optical remote sensing is limited. The SAR-inherent speckle effect remains an obstruction to SAR image interpretation and automated analysis. Recently, deep learning-based despeckling techniques such as supervised non-local methods have shown excellent speckle reduction for very high resolution (VHR) SAR data. Because the approaches have been mostly applied to VHR commercial SAR datasets, this work transfers, adapts and tests an existing deep learning-based non-local speckle filter on medium-resolution Sentinel-1 data. The results show that adaptations to the model lead to superior filtering results compared to conventional filters while simultaneously yielding better performance when the filtered data is used in a downstream task relevant to humanitarian application - dwelling change detection in forcibly displaced population settlement areas.