Fine-Scale Sea Ice Segmentation for High-Resolution Satellite Imagery with Weakly-Supervised CNNs
Fine-Scale Sea Ice Segmentation for High-Resolution Satellite Imagery with Weakly-Supervised CNNs
复制标题
DOI：
10.3390/rs13183562
复制
发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
B. Gonçalves;H. Lynch
中科院分区：
文献类型：
--
作者：
B. Gonçalves;H. Lynch
细尺度海冰条件是我们理解和模拟气候变化的关键。我们提出了第一个深度学习管道，用于从高分辨率卫星图像（Worldview-3）中提取精细尺度的海冰层。从图像中提取海冰通常是具有挑战性的，因为来自较老浮冰的潜在复杂纹理（即，漂浮的大块海冰）和周围的雪泥冰，使浮冰与周围的水不那么明显。我们提出了一个管道使用的U-Net变种与Resnet编码器检索浮冰像素掩模从非常高分辨率的多光谱卫星图像。即使使用中等大小的手动标记训练集和最基本的超参数选择，我们基于CNN的方法也获得了0.698的样本外F1分数-与分水岭分割基线相比，提高了近60%。然后，我们补充我们的训练集与一个更大的样本图像弱标记的分水岭分割算法。为了确保流域衍生的浮冰掩模能够很好地代表底层图像，我们为每个弱标记图像创建了一个合成版本，其中掩模之外的区域被开放的水域风景所取代。添加我们的合成图像数据集，与手工标记相比，以最小的努力获得，进一步提高了样本外F1得分为0.734。最后，我们使用了四个测试指标的集合，并在整个场景的拼接输出后进行了评估，以在模型选择期间模仿生产设置，达到了0.753的样本外F1得分。我们的全自动流水线能够以非常精细的细节水平检测，监测和分割浮冰，并为其他用例提供路线图，其中可以使用基于阈值的方法获得部分结果，但需要上下文鲁棒的分割流水线。
Fine-scale sea ice conditions are key to our efforts to understand and model climate change. We propose the first deep learning pipeline to extract fine-scale sea ice layers from high-resolution satellite imagery (Worldview-3). Extracting sea ice from imagery is often challenging due to the potentially complex texture from older ice floes (i.e., floating chunks of sea ice) and surrounding slush ice, making ice floes less distinctive from the surrounding water. We propose a pipeline using a U-Net variant with a Resnet encoder to retrieve ice floe pixel masks from very-high-resolution multispectral satellite imagery. Even with a modest-sized hand-labeled training set and the most basic hyperparameter choices, our CNN-based approach attains an out-of-sample F1 score of 0.698–a nearly 60% improvement when compared to a watershed segmentation baseline. We then supplement our training set with a much larger sample of images weak-labeled by a watershed segmentation algorithm. To ensure watershed derived pack-ice masks were a good representation of the underlying images, we created a synthetic version for each weak-labeled image, where areas outside the mask are replaced by open water scenery. Adding our synthetic image dataset, obtained at minimal effort when compared with hand-labeling, further improves the out-of-sample F1 score to 0.734. Finally, we use an ensemble of four test metrics and evaluated after mosaicing outputs for entire scenes to mimic production setting during model selection, reaching an out-of-sample F1 score of 0.753. Our fully-automated pipeline is capable of detecting, monitoring, and segmenting ice floes at a very fine level of detail, and provides a roadmap for other use-cases where partial results can be obtained with threshold-based methods but a context-robust segmentation pipeline is desired.