MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform

Spaceborne synthetic aperture radar (SAR) is a promising remote sensing technique, as it can produce high-resolution imagery over a wide area of surveillance with all-weather and all-day capabilities. However, the spaceborne SAR sensor may suffer from severe radio frequency interference (RFI) from s...

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Published in:Remote Sensing
Main Authors: Xiuhe Li, Jinhe Ran, Hao Zhang, Shunjun Wei
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Subjects:
Online Access:https://www.mdpi.com/2072-4292/14/24/6337
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author Xiuhe Li
Jinhe Ran
Hao Zhang
Shunjun Wei
author_facet Xiuhe Li
Jinhe Ran
Hao Zhang
Shunjun Wei
author_sort Xiuhe Li
collection DOAJ
container_title Remote Sensing
description Spaceborne synthetic aperture radar (SAR) is a promising remote sensing technique, as it can produce high-resolution imagery over a wide area of surveillance with all-weather and all-day capabilities. However, the spaceborne SAR sensor may suffer from severe radio frequency interference (RFI) from some similar frequency band signals, resulting in image quality degradation, blind spot, and target loss. To remove these RFI features presented on spaceborne SAR images, we propose a multi-dimensional calibration and suppression network (MCSNet) to exploit the features learning of spaceborne SAR images and RFI. In the scheme, a joint model consisting of the spaceborne SAR image and RFI is established based on the relationship between SAR echo and the scattering matrix. Then, to suppress the RFI presented in images, the main structure of MCSNet is constructed by a multi-dimensional and multi-channel strategy, wherein the feature calibration module (FCM) is designed for global depth feature extraction. In addition, MCSNet performs planned mapping on the feature maps repeatedly under the supervision of the SAR interference image, compensating for the discrepancies caused during the RFI suppression. Finally, a detailed restoration module based on the residual network is conceived to maintain the scattering characteristics of the underlying scene in interfered SAR images. The simulation data and Sentinel-1 data experiments, including different landscapes and different forms of RFI, validate the effectiveness of the proposed method. Both the results demonstrate that MCSNet outperforms the state-of-the-art methods and can greatly suppress the RFI in spaceborne SAR.
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spelling doaj-art-deeca5c19d784c778bd8b739600dbd5d2025-08-19T22:20:08ZengMDPI AGRemote Sensing2072-42922022-12-011424633710.3390/rs14246337MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature TransformXiuhe Li0Jinhe Ran1Hao Zhang2Shunjun Wei3Electronic Countermeasure Institute, National University of Defense Technology, Hefei 230037, ChinaElectronic Countermeasure Institute, National University of Defense Technology, Hefei 230037, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSpaceborne synthetic aperture radar (SAR) is a promising remote sensing technique, as it can produce high-resolution imagery over a wide area of surveillance with all-weather and all-day capabilities. However, the spaceborne SAR sensor may suffer from severe radio frequency interference (RFI) from some similar frequency band signals, resulting in image quality degradation, blind spot, and target loss. To remove these RFI features presented on spaceborne SAR images, we propose a multi-dimensional calibration and suppression network (MCSNet) to exploit the features learning of spaceborne SAR images and RFI. In the scheme, a joint model consisting of the spaceborne SAR image and RFI is established based on the relationship between SAR echo and the scattering matrix. Then, to suppress the RFI presented in images, the main structure of MCSNet is constructed by a multi-dimensional and multi-channel strategy, wherein the feature calibration module (FCM) is designed for global depth feature extraction. In addition, MCSNet performs planned mapping on the feature maps repeatedly under the supervision of the SAR interference image, compensating for the discrepancies caused during the RFI suppression. Finally, a detailed restoration module based on the residual network is conceived to maintain the scattering characteristics of the underlying scene in interfered SAR images. The simulation data and Sentinel-1 data experiments, including different landscapes and different forms of RFI, validate the effectiveness of the proposed method. Both the results demonstrate that MCSNet outperforms the state-of-the-art methods and can greatly suppress the RFI in spaceborne SAR.https://www.mdpi.com/2072-4292/14/24/6337synthetic aperture radarRFI suppressionthe Sentinel-1 data
spellingShingle Xiuhe Li
Jinhe Ran
Hao Zhang
Shunjun Wei
MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform
synthetic aperture radar
RFI suppression
the Sentinel-1 data
title MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform
title_full MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform
title_fullStr MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform
title_full_unstemmed MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform
title_short MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform
title_sort mcsnet a radio frequency interference suppression network for spaceborne sar images via multi dimensional feature transform
topic synthetic aperture radar
RFI suppression
the Sentinel-1 data
url https://www.mdpi.com/2072-4292/14/24/6337
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AT jinheran mcsnetaradiofrequencyinterferencesuppressionnetworkforspacebornesarimagesviamultidimensionalfeaturetransform
AT haozhang mcsnetaradiofrequencyinterferencesuppressionnetworkforspacebornesarimagesviamultidimensionalfeaturetransform
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