Automatic RFI Identification for Sentinel-1 Based on Siamese-Type Deep CNN Using Repeat-Pass Images

Lu, X; Wang, C; Xu, X; Yang, H; Zhang, S; Tan, K; Bao, X; Su, W; Gu, H

Automatic RFI Identification for Sentinel-1 Based on Siamese-Type Deep CNN Using Repeat-Pass Images

Lu, X Wang, C Xu, X

Yang, H Zhang, S Tan, K Bao, X Su, W Gu, H

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Geoscience and Remote Sensing, 2022, 60
Issue Date:: 2022-01-01

Closed Access

	Filename	Description	Size
	Automatic RFI Identification for Sentinel-1 Based on Siamese-Type Deep CNN Using Repeat-Pass Images.pdf	Published version	9.61 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lu, X
dc.contributor.author	Wang, C
dc.contributor.author	Xu, X https://orcid.org/0000-0002-3674-199X
dc.contributor.author	Yang, H
dc.contributor.author	Zhang, S
dc.contributor.author	Tan, K
dc.contributor.author	Bao, X
dc.contributor.author	Su, W
dc.contributor.author	Gu, H
dc.date.accessioned	2023-03-20T04:04:41Z
dc.date.available	2023-03-20T04:04:41Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Geoscience and Remote Sensing, 2022, 60
dc.identifier.issn	0196-2892
dc.identifier.issn	1558-0644
dc.identifier.uri	http://hdl.handle.net/10453/167748
dc.description.abstract	Since the start of the Sentinel-1 (S-1) mission, numerous cases of severe image degradation caused by radio frequency interference (RFI) have been reported, which puts forward an urgent need for RFI identification and mitigation. In this article, an automatic RFI identification method is proposed based on a Siamese-type deep convolutional neural network (Siam-CNN-RIM). The Siam-CNN-RIM can be served as a preprocessing step before RFI mitigation to identify whether an S-1 image is RFI-contaminated or not. Different from traditional RFI identification networks which only use a single image as input, an additional image in the repeat-pass time series is also fed into the input of Siam-CNN-RIM as a reference. Both the input images correspond to the same illuminated area and pass through the same convolutional layer followed by an energy function such that the different features caused by RFI can be extracted and the background terrain features can be ignored. This is beneficial for distinguishing the real RFI signatures and the similar terrain signatures that may cause false positives and thus improving the RFI identification performance. Experimental results show that the proposed method is robust in different scenarios and can achieve more than 97% RFI identification accuracy, even for the open-set task where the test scenarios are not included in the training set.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Geoscience and Remote Sensing
dc.relation.isbasedon	10.1109/TGRS.2022.3190488
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0404 Geophysics, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Automatic RFI Identification for Sentinel-1 Based on Siamese-Type Deep CNN Using Repeat-Pass Images
dc.type	Journal Article
utslib.citation.volume	60
utslib.for	0404 Geophysics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-20T04:04:40Z
pubs.publication-status	Published
pubs.volume	60

Abstract:

Since the start of the Sentinel-1 (S-1) mission, numerous cases of severe image degradation caused by radio frequency interference (RFI) have been reported, which puts forward an urgent need for RFI identification and mitigation. In this article, an automatic RFI identification method is proposed based on a Siamese-type deep convolutional neural network (Siam-CNN-RIM). The Siam-CNN-RIM can be served as a preprocessing step before RFI mitigation to identify whether an S-1 image is RFI-contaminated or not. Different from traditional RFI identification networks which only use a single image as input, an additional image in the repeat-pass time series is also fed into the input of Siam-CNN-RIM as a reference. Both the input images correspond to the same illuminated area and pass through the same convolutional layer followed by an energy function such that the different features caused by RFI can be extracted and the background terrain features can be ignored. This is beneficial for distinguishing the real RFI signatures and the similar terrain signatures that may cause false positives and thus improving the RFI identification performance. Experimental results show that the proposed method is robust in different scenarios and can achieve more than 97% RFI identification accuracy, even for the open-set task where the test scenarios are not included in the training set.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/167748