| 研究課題/領域番号 |
19J13500
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| 研究機関 | 東京大学 |
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研究代表者 |
郭 直霊 東京大学, 新領域創成科学研究科, 特別研究員(DC2)
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| 研究期間 (年度) |
2019-04-25 – 2021-03-31
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| キーワード | Deep Learning / Super Resolution / Remote Sensing |
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| 研究実績の概要 |
We address the challenging task of the semantic segmentation of land features via multi-source remote sensing imagery with different spatial resolutions. Unlike previous works that mainly focused on optimizing the segmentation model, we propose to integrate super-resolution techniques with the existing framework to enhance the segmentation performance. The results confirmed that the proposed method is a viable tool for building semantic segmentation, especially when the resolution is unaligned.
Publised papers:
1. Super-Resolution Integrated Building Semantic Segmentation for Multi-Source Remote Sensing Imagery
2. GeoSR: A Computer Vision Package for Deep Learning Based Single-Frame Remote Sensing Imagery Super-Resolution
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
The data source we use is based on satellite and aerial imagery, UAV has not been applied yet.
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| 今後の研究の推進方策 |
1. System testing
Carefully test the obtained system by considering the feasibility, stability, accuracy and robustness.
2. Practical application
Apply this system in real catastrophe condition if it’s possible, and contribute to real rescues.
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