| Project/Area Number |
20K19834
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 61010:Perceptual information processing-related
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| Research Institution | Institute of Physical and Chemical Research |
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Principal Investigator |
Baier Gerald 国立研究開発法人理化学研究所, 革新知能統合研究センター, 特別研究員 (80865618)
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| Project Period (FY) |
2020-04-01 – 2021-03-31
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| Project Status |
Discontinued (Fiscal Year 2020)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2022: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2021: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2020: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | image synthesis / multimodal dataset / deep learning / transfer learning / flood detection / synthetic aperture radar |
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| Outline of Research at the Start |
The motivation of this research is to reduce the labeling of training data for analyzing SAR images using deep learning. Detecting floods serves as a base line. Simulation of SAR images could help generate realistic training data. With transfer learning less new data needs to be collected.
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| Outline of Annual Research Achievements |
- 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.
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