Ryo Natsuaki 研究室
主宰者：Ryo Natsuaki
東京大学
AI 要約（直近 5 年の研究成果）
本研究室は、衛星搭載レーダを用いた地球観測技術の開発に取り組んでいます。特に、合成開口レーダ（SAR）の観測能力を向上させることを主な目標としており、高い空間解像度と広い観測範囲の同時実現、ノイズ除去、データ処理手法の改善などに関する研究を行っています。例えば、偏波レーダデータのノイズ除去、都市部の洪水検出、地盤変動の測定などといった実社会の課題に対して、複数の観測方向からのデータ取得やフィルタリング技術、干渉法を組み合わせたアプローチを採用しています。
具体的には、小型衛星による高性能観測を実現するためのアンテナ設計やレーダ信号波形の最適化、複数ベースライン干渉法による地形モデルの推定といった要素技術を開発しています。また、レーダ画像の処理には機械学習の一種である深層学習も活用し、複雑な信号を効率よく解析する手法を提案しています。さらに、既存のレーダシステムの課題として電波干渉への対策やモニタリングにも着手しており、観測システム全体の信頼性向上に貢献しています。加えて、レーダ信号を利用したモノのインターネット通信技術など、従来にない応用展開も進めており、衛星リモートセンシングの活用領域を拡張する研究が特徴です。
※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。
外部リンク
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研究成果（181 件）
- DOI: https://doi.org/10.1109/tgrs.2026.3695023
- DOI: https://doi.org/10.1109/jstars.2026.3695663
- [2026] IEEE GRSS Schools: Advancing Career Development and Promoting Impactful Educational InitiativesDOI: https://doi.org/10.1109/mgrs.2025.3645436
- DOI: https://doi.org/10.1109/tgrs.2026.3694335
- DOI: https://doi.org/10.1109/tgrs.2026.3694335
- DOI: https://doi.org/10.1109/tgrs.2026.3695023
- DOI: https://doi.org/10.1109/jstars.2026.3695663
- DOI: https://doi.org/10.1109/r10-htc63995.2025.11394041
- DOI: https://doi.org/10.1109/r10-htc63995.2025.11394041
- DOI: https://doi.org/10.23919/apsar64635.2025.11392318
続きを表示（残り 171 件）閉じる
- DOI: https://doi.org/10.23919/isap63122.2025.11361765
- DOI: https://doi.org/10.1109/jstars.2025.3617173
- DOI: https://doi.org/10.46620/ursiaprasc25/yfwi8175
- DOI: https://doi.org/10.23919/apsar64635.2025.11392435
- [2025] Degree-of-Polarization-Based Radio Frequency Interference Detection for Synthetic Aperture RadarDOI: https://doi.org/10.1109/tgrs.2025.3570493
- DOI: https://doi.org/10.1109/embc58623.2025.11254235
- DOI: https://doi.org/10.1109/jstars.2025.3617173
- DOI: https://doi.org/10.46620/ursiaprasc25/yfwi8175
- [2025] Degree-of-Polarization-Based Radio Frequency Interference Detection for Synthetic Aperture RadarDOI: https://doi.org/10.1109/tgrs.2025.3570493
- DOI: https://doi.org/10.1109/igarss55030.2025.11242514
- DOI: https://doi.org/10.1109/igarss55030.2025.11242514
- DOI: https://doi.org/10.1109/igarss55030.2025.11243852
- DOI: https://doi.org/10.1109/igarss55030.2025.11243844
- DOI: https://doi.org/10.1109/igarss55030.2025.11242331
- DOI: https://doi.org/10.1109/igarss55030.2025.11242840
- DOI: https://doi.org/10.1109/igarss55030.2025.11243351
- DOI: https://doi.org/10.1109/igarss55030.2025.11242840
- DOI: https://doi.org/10.1109/igarss55030.2025.11243351
- DOI: https://doi.org/10.1109/igarss55030.2025.11243852
- DOI: https://doi.org/10.1109/igarss55030.2025.11243844
- DOI: https://doi.org/10.1109/igarss55030.2025.11242331
- DOI: https://doi.org/10.23919/apsar64635.2025.11392544
- [2025] Ship Detection with Bistatic Interferometric SAR and Complex-Valued Convolutional Neural NetworksDOI: https://doi.org/10.23919/apsar64635.2025.11392334
- [2025] Three-Dimensional Interferometric Sar Time-Series Analysis with Acceleration-Aware EstimationDOI: https://doi.org/10.23919/apsar64635.2025.11392385
- DOI: https://doi.org/10.23919/apsar64635.2025.11392459
- DOI: https://doi.org/10.1109/lgrs.2025.3633588
- DOI: https://doi.org/10.1109/jstars.2025.3631900
- DOI: https://doi.org/10.23919/piers-fall62445.2025.11393994
- DOI: https://doi.org/10.1109/lgrs.2025.3633588
- DOI: https://doi.org/10.1109/jstars.2025.3631900
- DOI: https://doi.org/10.23919/piers-fall62445.2025.11393994
- DOI: https://doi.org/10.23919/isap63122.2025.11361765
- DOI: https://doi.org/10.23919/apsar64635.2025.11392318
- DOI: https://doi.org/10.23919/apsar64635.2025.11392549
- DOI: https://doi.org/10.23919/apsar64635.2025.11392459
- DOI: https://doi.org/10.23919/apsar64635.2025.11392263
- [2025] Three-Dimensional Interferometric Sar Time-Series Analysis with Acceleration-Aware EstimationDOI: https://doi.org/10.23919/apsar64635.2025.11392385
- [2025] Ship Detection with Bistatic Interferometric SAR and Complex-Valued Convolutional Neural NetworksDOI: https://doi.org/10.23919/apsar64635.2025.11392334
- DOI: https://doi.org/10.23919/apsar64635.2025.11392544
- DOI: https://doi.org/10.23919/apsar64635.2025.11392549
- DOI: https://doi.org/10.23919/apsar64635.2025.11392263
- DOI: https://doi.org/10.23919/apsar64635.2025.11392435
- [2024] Complex-Valued Neural-Network Inverse Mapping for Explainability in PolSAR/InSAR ApplicationsDOI: https://doi.org/10.1109/igarss53475.2024.10640643
- DOI: https://doi.org/10.1109/igarss53475.2024.10642008
- DOI: https://doi.org/10.1109/igarss53475.2024.10640790
- DOI: https://doi.org/10.1109/igarss53475.2024.10640790
- DOI: https://doi.org/10.1109/igarss53475.2024.10642919
- DOI: https://doi.org/10.1109/igarss53475.2024.10642919
- DOI: https://doi.org/10.1016/j.rse.2024.114426
- DOI: https://doi.org/10.1109/embc53108.2024.10782069
- DOI: https://doi.org/10.1109/igarss53475.2024.10642008
- DOI: https://doi.org/10.1016/j.rse.2024.114426
- DOI: https://doi.org/10.1109/embc53108.2024.10782069
- DOI: https://doi.org/10.1109/igarss53475.2024.10641712
- [2024] Comparison of Temporal Decorrelation Decay Functions Over Land Cover Types for L- and C-Band SARDOI: https://doi.org/10.1109/igarss53475.2024.10642568
- DOI: https://doi.org/10.1109/igarss53475.2024.10642365
- DOI: https://doi.org/10.1109/igarss53475.2024.10641242
- DOI: https://doi.org/10.1109/ijcnn60899.2024.10650795
- DOI: https://doi.org/10.1109/igarss53475.2024.10642365
- DOI: https://doi.org/10.1109/igarss53475.2024.10641242
- DOI: https://doi.org/10.1109/ijcnn60899.2024.10650795
- DOI: https://doi.org/10.1109/msp.2024.3384179
- DOI: https://doi.org/10.1109/tgrs.2024.3454766
- DOI: https://doi.org/10.1109/msp.2024.3384179
- DOI: https://doi.org/10.1109/tgrs.2024.3454766
- DOI: https://doi.org/10.1109/lgrs.2024.3359008
- DOI: https://doi.org/10.1109/jstars.2024.3454789
- DOI: https://doi.org/10.1109/tmlcn.2024.3485521
- DOI: https://doi.org/10.1109/lgrs.2024.3359008
- DOI: https://doi.org/10.1109/tmlcn.2024.3485521
- DOI: https://doi.org/10.1109/igarss52108.2023.10281938
- DOI: https://doi.org/10.1109/igarss52108.2023.10282137
- DOI: https://doi.org/10.1109/igarss52108.2023.10282723
- DOI: https://doi.org/10.1109/igarss52108.2023.10281784
- DOI: https://doi.org/10.1109/igarss52108.2023.10282317
- DOI: https://doi.org/10.1109/igarss52108.2023.10282066
- DOI: https://doi.org/10.1109/igarss52108.2023.10281938
- DOI: https://doi.org/10.1109/jstars.2023.3308049
- DOI: https://doi.org/10.1109/tnnls.2023.3291702
- DOI: https://doi.org/10.1109/ijcnn54540.2023.10191214
- DOI: https://doi.org/10.1109/ijcnn54540.2023.10191220
- DOI: https://doi.org/10.1109/mgrs.2023.3273083
- DOI: https://doi.org/10.1109/jstars.2023.3247788
- [2023] Towards a Standardized method to quantify the amount of interference in the remote sensing bandsDOI: https://doi.org/10.46620/ursigass.2023.3290.ixuk4420
- DOI: https://doi.org/10.1109/tnnls.2023.3291702
- DOI: https://doi.org/10.1109/ijcnn54540.2023.10191214
- DOI: https://doi.org/10.1109/mgrs.2023.3273083
- DOI: https://doi.org/10.1109/jstars.2023.3247788
- DOI: https://doi.org/10.1109/tgrs.2023.3293868
- DOI: https://doi.org/10.1109/apsar58496.2023.10388523
- [2023] Towards a Standardized method to quantify the amount of interference in the remote sensing bandsDOI: https://doi.org/10.46620/ursigass.2023.3290.ixuk4420
- DOI: https://doi.org/10.1109/tgrs.2023.3293868
- DOI: https://doi.org/10.1109/trs.2023.3259326
- DOI: https://doi.org/10.1109/trs.2023.3259326
- DOI: https://doi.org/10.1109/lgrs.2023.3346378
- DOI: https://doi.org/10.1186/s40645-023-00597-5
- DOI: https://doi.org/10.1109/apsar58496.2023.10388713
- DOI: https://doi.org/10.1109/lgrs.2023.3346378
- DOI: https://doi.org/10.1186/s40645-023-00597-5
- DOI: https://doi.org/10.1109/apsar58496.2023.10388713
- DOI: https://doi.org/10.1109/apsar58496.2023.10388523
- DOI: https://doi.org/10.1109/apsar58496.2023.10388723
- DOI: https://doi.org/10.1109/apsar58496.2023.10388834
- DOI: https://doi.org/10.1109/icetci58599.2023.10331315
- DOI: https://doi.org/10.23919/ursigass57860.2023.10265629
- DOI: https://doi.org/10.23919/ursigass57860.2023.10265570
- DOI: https://doi.org/10.1109/embc40787.2023.10341131
- DOI: https://doi.org/10.1109/igarss52108.2023.10281436
- [2023] Extending Observation Coverage of SAR by Separating Range Ambiguous Signals Using PN-sequencesDOI: https://doi.org/10.1109/igarss52108.2023.10283336
- DOI: https://doi.org/10.1109/apsar58496.2023.10388834
- DOI: https://doi.org/10.1109/icetci58599.2023.10331315
- DOI: https://doi.org/10.23919/ursigass57860.2023.10265570
- DOI: https://doi.org/10.1109/embc40787.2023.10341131
- DOI: https://doi.org/10.1109/igarss52108.2023.10281436
- [2023] Extending Observation Coverage of SAR by Separating Range Ambiguous Signals Using PN-sequencesDOI: https://doi.org/10.1109/igarss52108.2023.10283336
- [2023] Urban Damage Detection Using Temporally Stacked Synthetic Aperture Radar Interferometric CoherenceDOI: https://doi.org/10.1109/igarss52108.2023.10282496
- DOI: https://doi.org/10.1109/igarss52108.2023.10282137
- DOI: https://doi.org/10.1109/igarss52108.2023.10282723
- DOI: https://doi.org/10.1109/igarss52108.2023.10281784
- DOI: https://doi.org/10.1109/igarss52108.2023.10282317
- DOI: https://doi.org/10.1109/igarss52108.2023.10282066
- DOI: https://doi.org/10.1109/igarss46834.2022.9883827
- DOI: https://doi.org/10.1109/igarss46834.2022.9883656
- [2022] Proposal of PolSAR Land Classification Using Full-Learning Quaternion Convolutional Neural NetworksDOI: https://doi.org/10.1109/igarss46834.2022.9883874
- DOI: https://doi.org/10.23919/usnc-ursi52669.2022.9887445
- DOI: https://doi.org/10.1109/igarss46834.2022.9884056
- DOI: https://doi.org/10.1109/igarss46834.2022.9883656
- DOI: https://doi.org/10.1109/tgrs.2022.3186727
- [2022] Proposal of PolSAR Land Classification Using Full-Learning Quaternion Convolutional Neural NetworksDOI: https://doi.org/10.1109/igarss46834.2022.9883874
- DOI: https://doi.org/10.1109/igarss46834.2022.9883786
- DOI: https://doi.org/10.23919/usnc-ursi52669.2022.9887445
- DOI: https://doi.org/10.1109/sharc53093.2022.9720000
- DOI: https://doi.org/10.1109/jstars.2022.3164431
- DOI: https://doi.org/10.1109/access.2022.3184788
- DOI: https://doi.org/10.1109/jstars.2022.3164431
- DOI: https://doi.org/10.1109/tgrs.2022.3186727
- DOI: https://doi.org/10.1109/sharc53093.2022.9720000
- DOI: https://doi.org/10.1109/icetci55171.2022.9921365
- [2022] Predicting polarization state based on quaternion neural networks to facilitate channel predictionDOI: https://doi.org/10.1109/ijcnn55064.2022.9892509
- DOI: https://doi.org/10.1109/igarss46834.2022.9884409
- [2022] Predicting polarization state based on quaternion neural networks to facilitate channel predictionDOI: https://doi.org/10.1109/ijcnn55064.2022.9892509
- DOI: https://doi.org/10.1109/igarss46834.2022.9884409
- DOI: https://doi.org/10.1109/igarss46834.2022.9883827
- [2021] Spatial resolution of complex-valued reservoir computing in aspect classification using InSAR dataDOI: https://doi.org/10.1109/apsar52370.2021.9688400
- [2021] Spatial resolution of complex-valued reservoir computing in aspect classification using InSAR dataDOI: https://doi.org/10.1109/apsar52370.2021.9688400
- DOI: https://doi.org/10.1109/apsar52370.2021.9688438
- DOI: https://doi.org/10.1109/embc46164.2021.9630656
- DOI: https://doi.org/10.23919/isap47053.2021.9391413
- DOI: https://doi.org/10.23919/isap47053.2021.9391413
- DOI: https://doi.org/10.1109/igarss47720.2021.9554545
- DOI: https://doi.org/10.1109/igarss47720.2021.9554545
- DOI: https://doi.org/10.1109/igarss47720.2021.9553952
- DOI: https://doi.org/10.1109/jstars.2021.3102620
- DOI: https://doi.org/10.1109/apsar52370.2021.9688342
- DOI: https://doi.org/10.1109/embc46164.2021.9630656
- DOI: https://doi.org/10.1109/apsar52370.2021.9688438
- DOI: https://doi.org/10.1109/igarss47720.2021.9554751
- DOI: https://doi.org/10.1109/igarss47720.2021.9554751
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