Total-variation-regularized Tensor Ring Completion for Remote Sensing Image Reconstruction
Total-variation-regularized Tensor Ring Completion for Remote Sensing Image Reconstruction
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DOI：
10.1109/icassp.2019.8682696
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Wei He;Longhao Yuan;N. Yokoya
中科院分区：
文献类型：
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作者：
Wei He;Longhao Yuan;N. Yokoya
在最近的研究中，张量环（TR）分解已被证明是有效的数据压缩和表示。然而，现有的基于TR的补全方法只利用了视觉数据的全局低秩特性。将它们应用于遥感（RS）图像处理时，忽略了遥感图像中的空间信息。本文将TR分解引入到遥感图像处理中，提出了一种张量完备化的遥感图像重建方法。我们将全变分正则化的TR完成模型，同时利用低秩属性和空间连续性的RS图像。该算法采用增广的拉格朗日乘子法求解，在高光谱图像重建和多时相遥感图像云去除方面与现有算法相比具有上级性能。
In recent studies, tensor ring (TR) decomposition has shown to be effective in data compression and representation. However, the existing TR-based completion methods only exploit the global low-rank property of the visual data. When applying them to remote sensing (RS) image processing, the spatial information in the RS image is ignored. In this paper, we introduce the TR decomposition to RS image processing and propose a tensor completion method for RS image reconstruction. We incorporate the total-variation regularization into the TR completion model to exploit the low-rank property and spatial continuity of the RS image simultaneously. The proposed algorithm is solved by the augmented Lagrange multiplier method and has shown the superior performance in hyperspectral image reconstruction and multi-temporal RS image cloud removal against the state-of-the-art algorithms.