Detecting and Mitigating Radio Frequency Interference Artifacts via Tensor Decomposition of Multi-Temporal SAR Images

Siqi Lai, Yanyang Liu, Mingliang Tao, Jia Su, Ling Wang

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

Radio frequency interference (RFI) in space-based radar echo signals may affect the coherent focus imaging process, resulting in blurred scattered images or occlusion artifacts. Conventional echo domain RFI mitigation methods do not work well with image domain data. Therefore, a novel RFI mitigation method based on tensor low-rank sparse decomposition in the image domain is proposed in this paper. The tensor low-rank sparse decomposition problem can fully preserve the spatial correlation between images. A joint mathematical model of low-rank sparse tensor decomposition is established and solved to achieve the extraction and mitigation of remote sensing image interference. The results of Sentinel-lA data show that the method can extract interference artifacts and recover clear background images. The interference mitigation performance of this method is better compared with the previously proposed matrix decomposition method.

源语言英语
主期刊名2023 35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9789463968096
DOI
出版状态已出版 - 2023
活动35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023 - Sapporo, 日本
期限: 19 8月 202326 8月 2023

出版系列

姓名2023 35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023

会议

会议35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023
国家/地区日本
Sapporo
时期19/08/2326/08/23

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