论文标题
卫星图像的基于注意的散射网络
Attention-Based Scattering Network for Satellite Imagery
论文作者
论文摘要
来自堆叠光谱带或时空数据的多通道卫星图像具有各种大气特性的有意义表示。以有效的方式组合这些功能以创建表现且值得信赖的模型对预测者至关重要。神经网络表现出希望，但遭受了不直觉的计算，高级特征的融合，并且可能受到可用数据数量的限制。在这项工作中，我们利用散射变换来提取高级特征，而无需其他可训练的参数，并引入了分离方案，以引起人们对独立输入通道的关注。实验显示了估计热带气旋强度并预测卫星图像闪电的出现的有希望的结果。
Multi-channel satellite imagery, from stacked spectral bands or spatiotemporal data, have meaningful representations for various atmospheric properties. Combining these features in an effective manner to create a performant and trustworthy model is of utmost importance to forecasters. Neural networks show promise, yet suffer from unintuitive computations, fusion of high-level features, and may be limited by the quantity of available data. In this work, we leverage the scattering transform to extract high-level features without additional trainable parameters and introduce a separation scheme to bring attention to independent input channels. Experiments show promising results on estimating tropical cyclone intensity and predicting the occurrence of lightning from satellite imagery.
