3-D SAR Data-Driven Imaging via Learned Low-Rank and Sparse Priors

Wang, Mou; Wei, Shunjun; Zhou, Zichen; Shi, Jun; Guo, Yong‐Xin

doi:10.1109/tgrs.2022.3175486

Cited by 8 publications

(1 citation statement)

References 53 publications

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“…In fact, I X and I RF I deviate from each other in terms of the carried information and mechanism. Therefore, we consider them as independent and thus construct a convex optimization model [39,40] with l2 norm constraints [41] to solve the problem,…”

Section: The Proposed Methodsmentioning

confidence: 99%

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

Ran

Zhang

et al. 2022

Remote Sensing

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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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Section: The Proposed Methodsmentioning

confidence: 99%