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Open AccessArticle

RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

1
Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland
2
Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2919; https://doi.org/10.3390/s20102919
Received: 31 March 2020 / Revised: 17 May 2020 / Accepted: 19 May 2020 / Published: 21 May 2020
Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used. View Full-Text
Keywords: RFI; artefacts; InSAR; image processing; pixel convolution; thresholding; nearest neighbor filtering; deep learning RFI; artefacts; InSAR; image processing; pixel convolution; thresholding; nearest neighbor filtering; deep learning
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Chojka, A.; Artiemjew, P.; Rapiński, J. RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques. Sensors 2020, 20, 2919.

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