Abstract
The increasing intensity and frequency of floods is one of the many consequences of our changing climate. In this work, we explore ML techniques that improve the flood detection module of an operational early flood warning system. Our method exploits an unlabelled dataset of paired multi-spectral and Synthetic Aperture Radar (SAR) imagery to reduce the labeling requirements of a purely supervised learning method. Prior works have used unlabelled data by creating weak labels out of them. However, from our experiments we noticed that such a model still ends up learning the label mistakes in those weak labels. Motivated by knowledge distillation and semi supervised learning, we explore the use of a teacher to train a student with the help of a small hand labelled dataset and a large unlabelled dataset. Unlike the conventional self distillation setup, we propose a cross modal distillation framework that transfers supervision from a teacher trained on richer modality (multi-spectral images) to a student model trained on SAR imagery. The trained models are then tested on the Sen1Floods11 dataset. Our model outperforms the Sen1Floods11 baseline model trained on the weak labeled SAR imagery by an absolute margin of 6.53% Intersection-over-Union (IoU) on the test split.
Abstract (translated)
洪水的强度和频率的增加是我们气候变化的许多后果之一。在这个研究中,我们探讨了机器学习技术,以提高 operational early flood warning system 中的洪水检测模块。我们利用一个未标记的配对多光谱和合成孔径雷达图像的未命名数据集,以降低纯粹的监督学习方法的标记要求。以前的工作已经使用未标记数据,从它们中创建弱标签。然而,从我们的实验中我们发现,这样的模型仍然最终学习这些弱标签的标记错误。基于知识蒸馏和半监督学习的动机,我们探讨了使用一名教师帮助训练学生的方法,使用一个小手标注的数据集和一个大型未标注的数据集。与传统的自蒸馏setup不同,我们提出了一种跨modal蒸馏框架,将监督从训练丰富的modality(多光谱图像)转移到训练SAR图像的学生模型中。训练模型后,在Sen1Floods11数据集上进行了测试。我们的模型在弱标签SAR图像上的标记错误训练 Sen1Floods11 基线模型的相对误差6.53%的IoU上表现出色。
URL
https://arxiv.org/abs/2302.08180