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Miss data reconstruction in remote sensing images with a double weighted tensor low rank model

机译：双加权张量低秩模型在遥感影像中的小姐数据重构

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摘要

Missing data reconstruction (e.g., dead pixel repair and cloud removing) in remote sensing images is a very important problem for the subsequent image analysis. It is well-known that missing data reconstruction is an ill-posed problem. In remote sensing images, there is a strong correlation in spectral frequencies or in temporal frames, and also there are a lot of self-similarity patterns in spatial domain. We can make use of these properties to derive low rank matrices according to their spectral, temporal and spatial dimensions. In this paper, we propose a tensor completion model based on these low rank matrices to deal with missing data reconstruction problem. We also present a weighting method for spectral, temporal and spatial dimensions and for their distribution of singular values. Our experimental results demonstrate that the weighting method can recover remote images very well. In particular, we show the effectiveness of the proposed method for both simulated and real data sets, and the performance of the proposed in terms of visual and quantitative measures is better than those of the other testing methods.
机译：缺失遥感图像中的数据重建（例如，坏点修复和云去除）是后续图像分析中非常重要的问题。众所周知，丢失数据重建是一个不适定的问题。在遥感影像中，频谱频率或时间帧具有很强的相关性，在空间域中也存在很多自相似性模式。我们可以利用这些属性根据其光谱，时间和空间维度来推导低秩矩阵。在本文中，我们提出了一个基于这些低秩矩阵的张量完成模型，以处理丢失的数据重建问题。我们还为频谱，时间和空间维度及其奇异值分布提供了一种加权方法。我们的实验结果表明，加权方法可以很好地恢复远程图像。尤其是，我们展示了该方法对模拟和真实数据集的有效性，并且在视觉和定量度量方面的性能均优于其他测试方法。

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