论文标题
完全可逆的神经网络，用于大规模表面和地下表征通过遥感
Fully reversible neural networks for large-scale surface and sub-surface characterization via remote sensing
论文作者
论文摘要
当使用卷积神经网络进行（亚）表面表征时，高光谱和空气磁性和引力数据的大型空间/频率尺度会引起记忆问题。最近开发的完全可逆的网络可以通过对存储网络状态的低和固定内存要求，而不是深度典型的线性内存增长来避免内存限制。完全可逆的网络能够训练深度神经网络，这些神经网络占据整个数据量，并一口气创建语义分割。这种方法避免了需要在小补丁中工作或将数据补丁映射到仅中央像素的类。横向渗透损失函数需要进行小的修改，以与完全可逆的网络结合使用，并从稀疏采样的标签中学习，而没有看到完全标记的地面真相。我们显示了从高光谱延时数据中的土地利用变化检测中的例子，以及从空气地球物理和地质数据中的区域含水层映射。
The large spatial/frequency scale of hyperspectral and airborne magnetic and gravitational data causes memory issues when using convolutional neural networks for (sub-) surface characterization. Recently developed fully reversible networks can mostly avoid memory limitations by virtue of having a low and fixed memory requirement for storing network states, as opposed to the typical linear memory growth with depth. Fully reversible networks enable the training of deep neural networks that take in entire data volumes, and create semantic segmentations in one go. This approach avoids the need to work in small patches or map a data patch to the class of just the central pixel. The cross-entropy loss function requires small modifications to work in conjunction with a fully reversible network and learn from sparsely sampled labels without ever seeing fully labeled ground truth. We show examples from land-use change detection from hyperspectral time-lapse data, and regional aquifer mapping from airborne geophysical and geological data.
