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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Joint Interpolation of Multisensor Sea Surface Temperature Fields Using Nonlocal and Statistical Priors
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Joint Interpolation of Multisensor Sea Surface Temperature Fields Using Nonlocal and Statistical Priors

机译：基于非局部和统计先验的多传感器海面温度场联合插值

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摘要

This paper addresses the joint analysis of multisource and multiresolution remote sensing data for the interpolation of high-resolution sea surface geophysical fields. As case-study application, we consider the interpolation of sea surface temperature (SST) fields. We propose a novel statistical model that combines two key features: an exemplar-based prior and statistical priors. The exemplar-based prior, referred to as a nonlocal prior, exploits similarities between local patches (small field regions) to interpolate missing data areas from previously observed exemplars. This nonlocal prior also sets an explicit conditioning between the multisensor data. Two complementary statistical priors, namely a prior on the spatial covariance and a prior on the marginal distribution of the high-resolution details, are considered as sea surface geophysical fields that are expected to depict specific spectral and marginal features in relation to the underlying turbulent ocean dynamics. We report the experiments on both synthetic and real SST data. These experiments demonstrate the contributions of the proposed combination of nonlocal and statistical priors to interpolate visually consistent and geophysically sound SST fields from multisource satellite data. We further discuss the key features and parameterizations of this model as well as its relevance with respect to classical interpolation techniques.
机译：本文讨论了多源和多分辨率遥感数据的联合分析，用于插值高分辨率海表地球物理场。作为案例研究应用程序，我们考虑对海面温度（SST）场进行插值。我们提出了一个新颖的统计模型，该模型结合了两个关键特征：基于示例的先验和统计先验。基于样本的先验称为非局部先验，它利用局部补丁（小字段区域）之间的相似性来内插先前观察到的样本中缺失的数据区域。该非本地先验还设置了多传感器数据之间的显式条件。两个互补的统计先验，即空间协方差的先验和高分辨率细节的边缘分布的先验，被认为是海面地球物理场，有望描绘出与潜在湍流有关的特定光谱和边缘特征动力学。我们报告了关于合成和真实SST数据的实验。这些实验证明了将非本地先验和统计先验相结合的建议的作用，以便从多源卫星数据中插值视觉上一致且地球物理上合理的SST场。我们将进一步讨论该模型的关键特征和参数化，以及其与经典插值技术的相关性。

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