Abstract

The Canadian Shield, the world’s largest exposure of glaciated crystalline bedrock, is the most lake-rich region on Earth. Recent studies using high-resolution CubeSat satellite imagery have revealed its surface water hydrology to be surprisingly dynamic at fine spatial scales. Here we test whether super-resolution (SR), the resampling of coarse imagery to a finer-than-native resolution, can detect such changes. We degrade high-resolution Planet CubeSat images of the Shield, then resample the coarsened imagery back to its native resolution using both traditional cubic resampling and a generative adversarial network, a type of neural network often used for SR. To test classification accuracy from the generated SR imagery, we apply the same water classification to both resampling methods and find similar performance based on confusion matrices with the control case of high-resolution imagery. Next, we compare fine-scale shoreline mapping in SR imagery, cubic resampling, and in-situ field surveys. SR shorelines outperform those from cubic resampling, with an increase in the modified kappa coefficient from −0.070 to 0.073. Potential applications include improved mapping of Shield lakes and retroactive application of SR to coarser-resolution satellite datasets to infer historical changes in fine-scale surface water dynamics.

摘要

加拿大地盾是世界上最大的冰川结晶基岩暴露区，也是地球上湖泊最丰富的地区。最近使用高分辨率 CubeSat 卫星图像的研究表明，其地表水水文在精细空间尺度上具有惊人的动态。在这里，我们测试超分辨率 (SR)，将粗糙图像重新采样到比原生分辨率更精细的分辨率，是否可以检测到这种变化。我们降低了 Shield 的高分辨率 Planet CubeSat 图像，然后使用传统的三次重采样和生成对抗网络（一种常用于 SR 的神经网络）将粗化的图像重新采样回其原始分辨率。为了测试生成的 SR 图像的分类准确性，我们将相同的水分类应用于两种重采样方法，并基于具有高分辨率图像控制案例的混淆矩阵找到相似的性能。接下来，我们比较了 SR 影像、三次重采样和原位实地调查中的精细海岸线测绘。SR 海岸线优于三次重采样的海岸线，修改后的 kappa 系数从 -0.070 增加到 0.073。潜在的应用包括改进 Shield 湖泊的测绘和将 SR 追溯应用到较粗分辨率的卫星数据集，以推断精细尺度地表水动力学的历史变化。修正 kappa 系数从 -0.070 增加到 0.073。潜在的应用包括改进 Shield 湖泊的测绘和将 SR 追溯应用到较粗分辨率的卫星数据集，以推断精细尺度地表水动力学的历史变化。修正 kappa 系数从 -0.070 增加到 0.073。潜在的应用包括改进 Shield 湖泊的测绘和将 SR 追溯应用到较粗分辨率的卫星数据集，以推断精细尺度地表水动力学的历史变化。