论文标题
具有深度置换不变网络的超级分辨多阶梯分段
Super-resolved multi-temporal segmentation with deep permutation-invariant networks
论文作者
论文摘要
通过新的深度学习模型，从多个陈述卫星收购中获得的多图像超分辨率最近取得了巨大的成功。在本文中，我们通过研究超级分辨的推理问题，超越了较高分辨率的经典图像重建，即以比传感平台高的空间分辨率下的语义分割。我们扩展了最近提出的模型，这些模型利用了时间置换不变性，可以通过多分辨率融合模块来推断分割任务所需的丰富语义信息。本文介绍的模型最近赢得了AI4EO挑战赛在增强的Sentinel 2农业上。
Multi-image super-resolution from multi-temporal satellite acquisitions of a scene has recently enjoyed great success thanks to new deep learning models. In this paper, we go beyond classic image reconstruction at a higher resolution by studying a super-resolved inference problem, namely semantic segmentation at a spatial resolution higher than the one of sensing platform. We expand upon recently proposed models exploiting temporal permutation invariance with a multi-resolution fusion module able to infer the rich semantic information needed by the segmentation task. The model presented in this paper has recently won the AI4EO challenge on Enhanced Sentinel 2 Agriculture.
