论文标题
使用时间平滑度和稀疏性调节张量优化的遥远云消除遥感图像
Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization
论文作者
论文摘要
在遥感图像中，伴随云阴影的厚云的存在是一个很高的概率事件，这可能会影响后续处理的质量并限制应用程序的情况。因此，消除厚厚的云和云阴影以及恢复云污染的像素是必不可少的，以充分利用遥感图像。在本文中，提出了一种基于时间平滑度和稀疏性调查张量优化（TSSTO）的新型厚度云去除方法。 TSSTO的基本思想是，厚的云和云阴影不仅稀疏，而且沿着图像的水平和垂直方向平滑，而干净的图像沿图像之间的时间方向则光滑。因此，稀疏性规范用于提高云和云阴影的稀疏性，并应用单向总变化（UTV）正规化器以确保单向平滑度。本文利用乘数的交替方向方法来求解呈现的模型，并生成云和云阴影元素以及干净的元素。纯化的云和云阴影元素被净化以获取云区域和云阴影区域。然后，将原始污染图像的清洁区域替换为干净元件的相应区域。最后，选择参考图像以使用信息克隆方法重建云区域和云阴影区域的详细信息。在来自不同传感器和不同分辨率的模拟和真实云污染的图像上进行了一系列实验，结果证明了提出的TSSTSO方法的潜力，用于从定性和定量观点中删除云和云阴影。
In remote sensing images, the presence of thick cloud accompanying cloud shadow is a high probability event, which can affect the quality of subsequent processing and limit the scenarios of application. Hence, removing the thick cloud and cloud shadow as well as recovering the cloud-contaminated pixels is indispensable to make good use of remote sensing images. In this paper, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed. The basic idea of TSSTO is that the thick cloud and cloud shadow are not only sparse but also smooth along the horizontal and vertical direction in images while the clean images are smooth along the temporal direction between images. Therefore, the sparsity norm is used to boost the sparsity of the cloud and cloud shadow, and unidirectional total variation (UTV) regularizers are applied to ensure the unidirectional smoothness. This paper utilizes alternation direction method of multipliers to solve the presented model and generate the cloud and cloud shadow element as well as the clean element. The cloud and cloud shadow element is purified to get the cloud area and cloud shadow area. Then, the clean area of the original cloud-contaminated images is replaced to the corresponding area of the clean element. Finally, the reference image is selected to reconstruct details of the cloud area and cloud shadow area using the information cloning method. A series of experiments are conducted both on simulated and real cloud-contaminated images from different sensors and with different resolutions, and the results demonstrate the potential of the proposed TSSTO method for removing cloud and cloud shadow from both qualitative and quantitative viewpoints.
