Dealing with limited remote sensing training data by image synthesis and transfer learning applied to disaster flood mapping

通过图像合成和迁移学习处理有限的遥感训练数据应用于灾害洪水测绘

• 批准号: 20K19834
• 负责人: Baier Gerald
• 金额: $ 2.66万
• 依托单位: Institute of Physical and Chemical Research
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Early-Career Scientists
• 财政年份: 2020
• 资助国家: 日本
• 起止时间: 2020-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20K19834/
• 关键词: image synthesis; multimodal dataset; deep learning; transfer learning; flood detection; synthetic aperture radar

- Using a generative-adversarial-network (GAN) for remote sensing image synthesis from land cover maps and auxiliary raster information. A submitted journal paper is currently under review https://arxiv.org/abs/2011.11314- Creation of a multimodal dataset of remote sensing images and making it publicly available https://ieee-dataport.org/open-access/geonrw. The dataset can not only be used for image synthesis but also image segmentation.

- 使用生成对抗网络（GAN）根据土地覆盖图和辅助栅格信息合成遥感图像。目前正在审查提交的期刊论文 https://arxiv.org/abs/2011.11314 - 创建遥感图像多模态数据集并将其公开可用 https://ieee-dataport.org/open-access/geonrw。该数据集不仅可以用于图像合成，还可以用于图像分割。

项目成果

Building a Parallel Universe Image Synthesis from Land Cover Maps and Auxiliary Raster Data

• 发表时间: 2020-11
• 期刊: ArXiv
• 作者: Gerald Baier;Antonin Deschemps;M. Schmitt;N. Yokoya

Created multimodal dataset

创建多模态数据集

arXiv version of TGRS paper

TGRS 论文的 arXiv 版本