论文标题
地球观察的一种可解释的深层语义分割方法
An Interpretable Deep Semantic Segmentation Method for Earth Observation
论文作者
论文摘要
地球观察对于包括洪水反应在内的一系列人类活动是基本的，因为它为决策者提供了重要的信息。语义分割在将来自卫星的原始超光谱数据映射到人类可理解的形式中，将类标签分配给每个像素。在本文中，我们介绍了一种基于原型的可解释的深层语义分割（IDSS）方法，该方法既高度准确又可解释。它的参数的数量级比深网（例如U-NET）使用的参数数量少，并且可以通过人类明显解释。此处提出的IDSS提供了透明的结构，允许用户检查和审核该算法的决定。结果表明，IDS可以超过包括U-NET在内的其他算法，就IOU（与联合的交叉路口）总水并召回总水。我们将WorldFloods数据集用于我们的实验，并计划使用语义分割结果与面具结合了永久性水的面具来检测洪水事件。
Earth observation is fundamental for a range of human activities including flood response as it offers vital information to decision makers. Semantic segmentation plays a key role in mapping the raw hyper-spectral data coming from the satellites into a human understandable form assigning class labels to each pixel. In this paper, we introduce a prototype-based interpretable deep semantic segmentation (IDSS) method, which is highly accurate as well as interpretable. Its parameters are in orders of magnitude less than the number of parameters used by deep networks such as U-Net and are clearly interpretable by humans. The proposed here IDSS offers a transparent structure that allows users to inspect and audit the algorithm's decision. Results have demonstrated that IDSS could surpass other algorithms, including U-Net, in terms of IoU (Intersection over Union) total water and Recall total water. We used WorldFloods data set for our experiments and plan to use the semantic segmentation results combined with masks for permanent water to detect flood events.
