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Koslow, Wadim and Rack, Kathrin and Rüttgers, Alexander and Dell Amore, Luca and Rizzoli, Paola (2024) Artifact detection in SAR images with AI methods. EUSAR 2024, 2024-04-23 - 2024-04-26, München, Deutschland. (Submitted)
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Abstract
The increasing number of Earth observation data necessitates for advanced automated evaluation. Autoencoders (AE), which are deep neural networks, have been successfully applied to change detection on optical images. Here, we present an investigation of the applicability of three different convolutional AE methods for change detection on time series of SAR images. During the evaluation, the so-called joint AE approach is proved to be more precise and less sensitive to changes in brightness, thus designating less false positives. Moreover, the joint AE method indicates three noticeable and conspicuous regions.
| Item URL in elib: | https://elib.dlr.de/199891/ | ||||||||||||||||||||||||
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| Document Type: | Conference or Workshop Item (Speech) | ||||||||||||||||||||||||
| Title: | Artifact detection in SAR images with AI methods | ||||||||||||||||||||||||
| Authors: |
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| Date: | 2024 | ||||||||||||||||||||||||
| Refereed publication: | No | ||||||||||||||||||||||||
| Open Access: | No | ||||||||||||||||||||||||
| Gold Open Access: | No | ||||||||||||||||||||||||
| In SCOPUS: | No | ||||||||||||||||||||||||
| In ISI Web of Science: | No | ||||||||||||||||||||||||
| Status: | Submitted | ||||||||||||||||||||||||
| Keywords: | SAR, Machine Learning, Anomaly Detection, Change Detection | ||||||||||||||||||||||||
| Event Title: | EUSAR 2024 | ||||||||||||||||||||||||
| Event Location: | München, Deutschland | ||||||||||||||||||||||||
| Event Type: | international Conference | ||||||||||||||||||||||||
| Event Start Date: | 23 April 2024 | ||||||||||||||||||||||||
| Event End Date: | 26 April 2024 | ||||||||||||||||||||||||
| HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||||||||||
| HGF - Program: | Space | ||||||||||||||||||||||||
| HGF - Program Themes: | Earth Observation | ||||||||||||||||||||||||
| DLR - Research area: | Raumfahrt | ||||||||||||||||||||||||
| DLR - Program: | R EO - Earth Observation | ||||||||||||||||||||||||
| DLR - Research theme (Project): | R - Impulse project Resilient supply infrastructure and flows of goods in the context of extreme weather events near the coast | ||||||||||||||||||||||||
| Location: | Köln-Porz , Oberpfaffenhofen | ||||||||||||||||||||||||
| Institutes and Institutions: | Institute of Software Technology > High-Performance Computing Institute of Software Technology Microwaves and Radar Institute > Spaceborne SAR Systems | ||||||||||||||||||||||||
| Deposited By: | Koslow, Wadim | ||||||||||||||||||||||||
| Deposited On: | 03 Jan 2024 10:41 | ||||||||||||||||||||||||
| Last Modified: | 24 Apr 2024 21:00 |
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- Artifact detection in SAR images with AI methods. (deposited 03 Jan 2024 10:41) [Currently Displayed]
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