Blind cloud and cloud shadow removal of multitemporal images based on total variation regularized low-rank sparsity decomposition
Blind cloud and cloud shadow removal of multitemporal images based on total variation regularized low-rank sparsity decomposition
复制标题
基于全变差正则低秩稀疏分解的多时相图像盲云和云影去除
DOI：
10.1016/j.isprsjprs.2019.09.003
复制
发表时间：
2019
影响因子：
12.7
通讯作者：
and T.-Z. Huang
中科院分区：
文献类型：
--
作者：
Y. Chen;W. He;N. Yokoya;and T.-Z. Huang
多时相卫星图像的云和云阴影去除是一项具有挑战性的任务，也是后续信息提取的重要研究方向。将云/阴影区域视为缺失信息，基于低秩矩阵/张量完成的方法被流行用于恢复经历云/阴影退化的信息。然而，现有的方法需要预先确定云/阴影的位置，并未能完全利用云/阴影区域的潜在信息。在这项研究中，我们提出了一个盲目的云/阴影去除方法的时间序列遥感图像云/阴影检测和去除统一在一起。首先，将退化图像分解为低秩的干净图像（表面反射）分量和稀疏（云/阴影）分量，可以同时完整地利用这两个分量的底层特征。同时，空间光谱全变分正则化的引入，以提高云/阴影分量的空间光谱连续性。其次，云/阴影位置检测从稀疏分量使用阈值方法。最后，我们采用云/阴影检测结果来指导从原始观测图像的信息补偿，以更好地保留在云/阴影的位置的信息。所提出的模型的问题是有效地解决使用交替方向方法的乘数。模拟和真实的数据集进行云/阴影检测和去除的方法相比，其他国家的最先进的方法，以证明我们的方法的有效性。
Cloud and cloud shadow (cloud/shadow) removal from multitemporal satellite images is a challenging task and has elicited much attention for subsequent information extraction. Regarding cloud/shadow areas as missing information, low-rank matrix/tensor completion based methods are popular to recover information undergoing cloud/shadow degradation. However, existing methods required to determine the cloud/shadow locations in advance and failed to completely use the latent information in cloud/shadow areas. In this study, we propose a blind cloud/shadow removal method for time-series remote sensing images by unifying cloud/shadow detection and removal together. First, we decompose the degraded image into low-rank clean image (surface-reflected) component and sparse (cloud/shadow) component, which can simultaneously and completely use the underlying characteristics of these two components. Meanwhile, the spatial-spectral total variation regularization is introduced to promote the spatial-spectral continuity of the cloud/shadow component. Second, the cloud/shadow locations are detected from the sparse component using a threshold method. Finally, we adopt the cloud/shadow detection results to guide the information compensation from the original observed images to better preserve the information in cloud/shadow-free locations. The problem of the proposed model is efficiently addressed using the alternating direction method of multipliers. Both simulated and real datasets are performed to demonstrate the effectiveness of our method for cloud/shadow detection and removal when compared with other state-of-the-art methods.