Abstract
We propose a new tiling strategy, Flip-n-Slide, which has been developed for specific use with large Earth observation satellite images when the location of objects-of-interest (OoI) is unknown and spatial context can be necessary for class disambiguation. Flip-n-Slide is a concise and minimalistic approach that allows OoI to be represented at multiple tile positions and orientations. This strategy introduces multiple views of spatio-contextual information, without introducing redundancies into the training set. By maintaining distinct transformation permutations for each tile overlap, we enhance the generalizability of the training set without misrepresenting the true data distribution. Our experiments validate the effectiveness of Flip-n-Slide in the task of semantic segmentation, a necessary data product in geophysical studies. We find that Flip-n-Slide outperforms the previous state-of-the-art augmentation routines for tiled data in all evaluation metrics. For underrepresented classes, Flip-n-Slide increases precision by as much as 15.8%.
Abstract (translated)
我们提出了一个新的镶嵌策略，名为翻转-滑动（Flip-n-Slide），专门用于在物体兴趣点（OoI）位置未知且需要空间上下文进行分类时的大型地球观测卫星图像。翻转-滑动是一种简洁而 minimalistic 的方法，允许 OoI 在多个贴图位置和方向上表示。这种策略引入了多个空间上下文信息视图，而不会引入训练集中的冗余。通过保持每个贴图覆盖的变换变换，我们增强了训练集的泛化能力，同时没有误解真实数据分布。我们的实验验证了翻转-滑动在语义分割任务中的有效性，这是地球物理学研究中的必要数据产品。我们发现，翻转-滑动在所有评估指标上都优于前 state-of-the-art 增强方法。对于代表性不足的类别，翻转-滑动将精度提高了 15.8%。
URL
https://arxiv.org/abs/2404.10927
