DOI: 10.57760/sciencedb.radars.00064
title: SAR images for Interference Detection and Suppression
title: SAR图像域干扰检测与抑制数据集

description: With the increasing congestion of the global electromagnetic spectrum and the complex electromagnetic countermeasure environments in certain regions, the interference problem faced by Synthetic Aperture Radar (SAR) systems has become increasingly severe. Such interference typically manifests as strip-like or block-shaped artifacts in SAR images, severely degrading image quality and posing a significant threat to SAR remote sensing applications. SAR interference suppression technology began to attract academic attention in the 1990s and has since gradually developed into one of the research hotspots in this field. In the last decade or so, with the rapid development of global SAR satellites, represented by the European Space Agency’s C-band Sentinel-1 series, more complex and diverse interference phenomena have been revealed in massive amounts of observational data, driving the field into a new stage of research and giving rise to numerous innovative processing methods. In recent years, our editorial team has conducted research on interference feature modeling, interpretation analysis, and interference detection and suppression in the SAR image domain. We have selected a series of typical single-look complex (SLC) images containing interference from over 100,000 Sentinel-1 images to construct this dataset, which covers a variety of interference types and parameters. This dataset aims to provide a reliable data benchmark for researchers and engineers in the field of SAR signal processing to facilitate the development, verification, and evaluation of technologies for SAR interference characterization, detection, and suppression. Furthermore, the comprehensive coverage of interference types in this dataset makes it a valuable data source and practical reference for studying information extraction (such as target detection, recognition, and classification) under typical interference conditions.Citation format:[1] YANG Huizhang, LANG Ping, YIN Junjun, et al. SAR images for Interference Detection and Suppression[OL]. Journal of Radars, 2025. https://radars.ac.cn/web/data/getData?dataType=SAR-IIDSEN&pageType=en
description: With the increasing congestion of the global electromagnetic spectrum and the complex electromagnetic countermeasure environments in certain regions, the interference problem faced by Synthetic Aperture Radar (SAR) systems has become increasingly severe. Such interference typically manifests as strip-like or block-shaped artifacts in SAR images, severely degrading image quality and posing a significant threat to SAR remote sensing applications. SAR interference suppression technology began to attract academic attention in the 1990s and has since gradually developed into one of the research hotspots in this field. In the last decade or so, with the rapid development of global SAR satellites, represented by the European Space Agency’s C-band Sentinel-1 series, more complex and diverse interference phenomena have been revealed in massive amounts of observational data, driving the field into a new stage of research and giving rise to numerous innovative processing methods. In recent years, our editorial team has conducted research on interference feature modeling, interpretation analysis, and interference detection and suppression in the SAR image domain. We have selected a series of typical single-look complex (SLC) images containing interference from over 100,000 Sentinel-1 images to construct this dataset, which covers a variety of interference types and parameters. This dataset aims to provide a reliable data benchmark for researchers and engineers in the field of SAR signal processing to facilitate the development, verification, and evaluation of technologies for SAR interference characterization, detection, and suppression. Furthermore, the comprehensive coverage of interference types in this dataset makes it a valuable data source and practical reference for studying information extraction (such as target detection, recognition, and classification) under typical interference conditions.Citation format:[1] YANG Huizhang, LANG Ping, YIN Junjun, et al. SAR images for Interference Detection and Suppression[OL]. Journal of Radars, 2025. https://radars.ac.cn/web/data/getData?dataType=SAR-IIDSEN&pageType=en