论文标题
SEN12MS-CR-TS：用于多模式多阶段云的遥感数据集
SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal
论文作者
论文摘要
大约一半是通过太空传播卫星收集的所有光学观察结果都受雾化或云的影响。因此，云覆盖范围会影响遥感从业者对我们星球进行连续无缝监测的能力。这项工作通过提出一个称为SEN12MS-CR-TS的新型多模式和多时间数据集，解决了光学卫星图像重建和去除云的挑战。我们提出了两个模型，强调了SEN12MS-CR-TS的好处和用例：首先，是一种多模式多阶段3D-Convolution神经网络，该神经网络可预测一系列云状光学图像和雷达图像的序列。其次，一个序列到序列翻译模型，可预测云覆盖时间序列的无云时间序列。两种方法均通过实验评估，其各自的模型对SEN12MS-CR-TS进行了训练和测试。进行的实验突出了我们的数据集对遥感社区的贡献以及多模式和多时间信息对重建噪声信息的好处。我们的数据集可在https://patricktum.github.io/cloud_removal上找到
About half of all optical observations collected via spaceborne satellites are affected by haze or clouds. Consequently, cloud coverage affects the remote sensing practitioner's capabilities of a continuous and seamless monitoring of our planet. This work addresses the challenge of optical satellite image reconstruction and cloud removal by proposing a novel multi-modal and multi-temporal data set called SEN12MS-CR-TS. We propose two models highlighting the benefits and use cases of SEN12MS-CR-TS: First, a multi-modal multi-temporal 3D-Convolution Neural Network that predicts a cloud-free image from a sequence of cloudy optical and radar images. Second, a sequence-to-sequence translation model that predicts a cloud-free time series from a cloud-covered time series. Both approaches are evaluated experimentally, with their respective models trained and tested on SEN12MS-CR-TS. The conducted experiments highlight the contribution of our data set to the remote sensing community as well as the benefits of multi-modal and multi-temporal information to reconstruct noisy information. Our data set is available at https://patrickTUM.github.io/cloud_removal
