A Kurtosis-Based Approach to Detect RFI in SMOS Image Reconstruction Data Processor

Khazaal A.; Cabot F.; Anterrieu E.; Soldo Y.

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A Kurtosis-Based Approach to Detect RFI in SMOS Image Reconstruction Data Processor

机译：基于峰度的SMOS图像重建数据处理器中的RFI检测方法

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The Soil Moisture and Ocean Salinity (SMOS) mission is a European Space Agency project aimed to observe two important geophysical variables, i.e., soil moisture over land and ocean salinity by L-band microwave imaging radiometry. This work is concerned with the contamination of the SMOS data by radio-frequency interferences (RFIs), which degrades the performance of the mission. In this paper, we propose an approach that detects if a given snapshot is contaminated, or not, by RFI. This approach is based on evaluating the kurtosis of each snapshot or data set, using all interferometric measurements provided by the instrument. The obtained kurtosis is considered as an indicator on how much the snapshot is polluted by RFI, thus allowing the user to decide on whether to keep or discard it.

机译：土壤水分和海洋盐度（SMOS）任务是欧洲航天局的一个项目，旨在观察两个重要的地球物理变量，即通过L波段微波成像辐射法观察陆地上的土壤水分和海洋盐度。这项工作涉及射频干扰（RFI）对SMOS数据的污染，这会降低任务的性能。在本文中，我们提出了一种检测给定快照是否被RFI污染的方法。该方法基于使用仪器提供的所有干涉测量来评估每个快照或数据集的峰度的方法。所获得的峰度被认为是快照被RFI污染的指标，从而使用户可以决定保留还是丢弃该快照。

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《IEEE Transactions on Geoscience and Remote Sensing》 |2014年第11期|7038-7047|共10页
作者
Khazaal A.; Cabot F.; Anterrieu E.; Soldo Y.;
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Kurtosis; radio-frequency interference (RFI); radiometry; remote sensing; synthetic aperture imaging;

机译：峰度;射频干扰（RFI）;放射线照相法;遥感;合成孔径成像;

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1. Improving the Spatial Bias Correction Algorithm in SMOS Image Reconstruction Processor: Validation of Soil Moisture Retrievals WithIn SituData [J] . Ali Khazâal, Philippe Richaume, François Cabot, IEEE Transactions on Geoscience and Remote Sensing. . 2019,第1期

机译：改进SMOS图像重建处理器中的空间偏差校正算法：使用 Initial 数据验证土壤水分检索
2. A Sparsity-Based Variational Approach for the Restoration of SMOS Images From L1A Data [J] . Javier Preciozzi, Andrés Almansa, Pablo Musé, Geoscience and Remote Sensing, IEEE Transactions on . 2017,第5期

机译：基于稀疏的变分方法从L1A数据恢复SMOS图像
3. Effect of the Polarization Leakage on the SMOS Image Reconstruction Algorithm: Validation Using Ocean Model and In Situ Soil Moisture Data [J] . Khazaal Ali, Leroux Delphine J., Cabot Francois, Geoscience and Remote Sensing, IEEE Transactions on . 2015,第9期

机译：极化泄漏对SMOS图像重建算法的影响：使用海洋模型和就地土壤水分数据进行验证
4. Review of the image reconstruction techniques used in SMOS data processing [C] . Gutierrez Antonio, Castro Rita, Barbosa Jose, IEEE International Geoscience and Remote Sensing Symposium . 2012

机译：审查SMOS数据处理中使用的图像重建技术
5. Signal processing techniques for improving image reconstruction of parallel Magnetic Resonance Imaging and dynamic Magnetic Resonance Imaging [D] . She, Huajun 2015

机译：用于改善并行磁共振成像和动态磁共振成像的图像重建的信号处理技术
6. Influence of Sinogram Affirmed Iterative Reconstruction of CT Data on Image Noise Characteristics and Low-Contrast Detectability: An Objective Approach [O] . Christian von Falck, Vesela Bratanova, Thomas Rodt, 2010

机译：客观地确认Singram确认的CT数据迭代重建对图像噪声特征和低对比度可检测性的影响
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8. Reconstruction of Tomographic Images from Sparse Data Sets By a New Finite Element Maximum Entropy Approach [R] . Smith, R. T., Zoltani, C. K., Klem, G. J. 1987

机译：利用新的有限元最大熵方法重建稀疏数据集中的层析成像

A Kurtosis-Based Approach to Detect RFI in SMOS Image Reconstruction Data Processor

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