With the development of satellite constellations and fierce competition for limited spectrum resources, Mutual Terrain Scattered Interference (MTSI) has become an emerging issue for spaceborne SAR systems. Existing mitigation methods mainly focus on strong wideband MTSI that satisfies the low-rank property. However, in most scenarios, MTSI presents as weak wideband or ultra-wideband interference occupying most of the spectrum, violating the low-rank assumption. This paper introduces two mitigation schemes employing the multi-view subspace representation to tackle these challenges. The first scheme divides the spectrum to construct a clean dictionary composed of subspaces with high correlation, which makes it possible to mitigate wideband MTSI by sparse constraint. Further, based on the differences in amplitude statistical characteristics between polluted and clean pulses, the second scheme utilizes histogram normalization to construct a clean dictionary from the polluted spectrum. Therefore, the wideband and ultrawideband MTSI could be mitigated by solving the subspace clustering problem. Experimental results in the simulated and real measured Sentinel-1 and GaoFen-3 data demonstrate superior image quality improvement by the proposed mitigation schemes.

Range ambiguity is a common issue for spaceborne synthetic aperture radar (SAR) systems, leading to image quality degradation and subsequent interpretation accuracy. Existing range ambiguity suppression methods mainly rely on raw echo domain processing with specific requirements on prior knowledge. However, most users can only obtain single-look-complex(SLC) products instead of raw echo products. Raw echo obtained from SLC through an inverse focusing process will waste additional time and space resources. This paper proposes a novel scheme to detect and suppress range ambiguity in SLC products to deal with this deficiency. The proposed method designs a detector to extract and suppress the focused range ambiguity based on the characteristic difference between the desired signal and range ambiguity in the fractional transform domain of the SLC image. Experimental results on real measured L-band spaceborne interferometry SAR system verify the detection performance of the proposed method, which is beneficial for subsequent land monitoring applications.

Radio frequency interference (RFI) is a significant threat to accurate microwave remote sensing. The RFI signals manifest themselves in unpredictable locations and patterns in the image, which will cause measurement distortion, image degradation, or even lead to wrong retrievals of the geophysical parameters. Accurate detection of RFI artifacts is a prerequisite step to preserve the overall quality of remote sensing quality. In this paper, a semantic cognitive enhancement network for RFI signature detection is proposed. It employs an encoder-decoder architecture, which incorporates the atrous spatial pyramid pooling, Depthwise convolution, and self-attentional mechanism. Rather than detecting the existence of RFI artifacts for an entire image, the proposed scheme can realize RFI recognition in a pixel-wise manner without setting predefined thresholds. Extensive experimental results on diverse scenarios in Sentinel-1 images with various RFI types are provided, which demonstrates robust detection performance for both strong and weak interference without requiring a large number of training samples.

Radio frequency interference (RFI) is a critical issue for accurate remote sensing by synthetic aperture radar (SAR). Existing literature mainly detects and mitigates RFI in the raw data domain, which is generally not accessible to the end-user. In this article, a novel RFI extraction and mitigation scheme in the image domain is proposed using multitemporal analysis of SAR images. By exploiting the coupling correlation and complementary information among the time-series images, the background landscape could be modeled as relatively stationary with the low-rank property. Meanwhile, the radiometric artifacts corresponding to RFI could be well extracted and characterized by the sparse components. Extraction and mitigation of RFI signatures could be achieved simultaneously via a joint iterative optimization process. Experimental results on typical real-measured Sentinel-1 datasets acquired in different regional areas with various RFI types demonstrate the validity of the proposed method.

Radio frequency interference (RFI) has been a pervasive and critical issue for space-borne synthetic aperture radar (SAR). The presence of RFI could lead to incorrect image interpretation and biased parameter retrieval, making the RFI detection a necessity to preserve overall data quality. In this paper, we propose an approach for detecting RFI signals in SAR raw data using time-frequency semantic analysis. Employing the U-Net convolutional neural network enables identification of target echoes and RFI signatures in 2D time-frequency representation with high probability. The detection process is realized without setting predefined thresholds, and could achieve superior performance without requiring large number of training samples.