| Project/Area Number |
16K01285
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | Niigata University |
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Principal Investigator |
Sato Ryoichi 新潟大学, 人文社会科学系, 教授 (00293184)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2018: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2017: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 地震 / 洪水 / 被災橋梁 / 冠水・浸水自動車 / 被災状況把握 / レーダポーラリメトリ / 合成開口レーダ / マイクロ波リモートセンシング / 被災地観測 / 自然災害 / リモートセンシング |
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| Outline of Final Research Achievements |
In this research, we developed useful techniques for accurately understanding the situation of stricken and flooded areas, by making full use of quad-pol SAR data. Here, two useful methods are proposed for (1) detection of completely/partially destroyed manmade objects after big earthquake, and (2) detection of roads where a vehicle is trapped in flooded urban area. It was found from the results of PolSAR image analysis using actual ALOS-2/PALSAR-2 data that the detection method (1) works well around the huge damage area in Kumamoto-Oita earthquake. Also, for the detection method (2), it was verified for the quad-pol SAR data acquired by polarimetric scattering measurement for a scaled vehicle-buildings model in anechoic chamber that the detection method is effective when the trapped vehicle is set in both lit and shadow regions.
Furthermore, examinations on understanding of the situations of stricken bridge, manmade objects involved in landslide or covered by snow were carried out.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で構築した新たな被災住宅群検出手法、および救助用陸路と洪水被災自動車の検出手法が実用化されれば、環境(昼夜・天候状態の違い)に依存せずに従来困難だった被災直後の緊急救助ルートの迅速な検出が可能となるため、緊急救助・災害監視・被害予想・分析・対策の分野に大きく貢献できる。また、高分解能のカラー画像で解析結果を提供するので、高度な専門知識なしに被災状況を理解しやすく、災害現場での有効な活用が期待できる。
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