| 講演名 | 2023-12-08 Towards Satellite Data Fusion For Active Fire Detection Nur Fajar Trihantoro(RMIT大), Simon Jones(RMIT大), Karin Reinke(RMIT大), |
|---|---|
| PDFダウンロードページ |  PDFダウンロードページへ |
| 抄録(和) | Numerous Earth observation satellite instruments show potential for monitoring wildfire, yet current fire detection algorithms mostly rely on data from a single sensor. This highlights an opportunity to explore a multi-sensor approach, considering the abundance of available Earth observation data. This study introduces a novel data-fusion-based algorithm utilizing Earth observation satellite data. It aims to create a reliable fire detection system, merging middle infrared data from geostationary sensors like Himawari-8/9 AHI, GeoKompsat-2 AMI, and low earth orbit sensors like Sentinel-3 SLSTR. The Kalman filter is the data fusion method, which enhances data accuracy by filtering out noise. Following Roberts and Wooster's [1] approach, a Diurnal Temporal Cycle model provides background temperature information. Preliminary results show promise, with detection rates exceeding 95% for various scenarios, highlighting the algorithm's potential. The presented approach showcases a promising advancement in active fire detection, combining the benefits of data fusion and conventional methodology for improved accuracy and adaptability in disaster management scenarios. The method's sensor agnosticism, iterative detection process, and consideration of resolution discrepancies mark significant advancements, addressing limitations observed in existing fire detection methodologies. Future steps involve refining the algorithm's performance across varied study cases, addressing spatial and temporal resolution disparities among input sensors, and scaling the method for near real-time implementation to enhance its utility in disaster response further. |
| 抄録(英) | Numerous Earth observation satellite instruments show potential for monitoring wildfire, yet current fire detection algorithms mostly rely on data from a single sensor. This highlights an opportunity to explore a multi-sensor approach, considering the abundance of available Earth observation data. This study introduces a novel data-fusion-based algorithm utilizing Earth observation satellite data. It aims to create a reliable fire detection system, merging middle infrared data from geostationary sensors like Himawari-8/9 AHI, GeoKompsat-2 AMI, and low earth orbit sensors like Sentinel-3 SLSTR. The Kalman filter is the data fusion method, which enhances data accuracy by filtering out noise. Following Roberts and Wooster's [1] approach, a Diurnal Temporal Cycle model provides background temperature information. Preliminary results show promise, with detection rates exceeding 95% for various scenarios, highlighting the algorithm's potential. The presented approach showcases a promising advancement in active fire detection, combining the benefits of data fusion and conventional methodology for improved accuracy and adaptability in disaster management scenarios. The method's sensor agnosticism, iterative detection process, and consideration of resolution discrepancies mark significant advancements, addressing limitations observed in existing fire detection methodologies. Future steps involve refining the algorithm's performance across varied study cases, addressing spatial and temporal resolution disparities among input sensors, and scaling the method for near real-time implementation to enhance its utility in disaster response further. |
| キーワード(和) | Kalman Filter / Data Fusion / Wildfire Detection / Earth Observation / Brightness Temperature / Middle Infrared / Diurnal Temperature Cycle |
| キーワード(英) | Kalman Filter / Data Fusion / Wildfire Detection / Earth Observation / Brightness Temperature / Middle Infrared / Diurnal Temperature Cycle |
| 資料番号 | SANE2023-65 |
| 発行日 | 2023-11-30 (SANE) |
| 研究会情報 | |
| 研究会 | SANE |
|---|---|
| 開催期間 | 2023/12/7(から3日開催) |
| 開催地（和） | インドネシア ジャワ県ソロ市 |
| 開催地（英） | Surakarta, Indonesia |
| テーマ（和） | ICSANE |
| テーマ（英） | ICSANE |
| 委員長氏名（和） | 若山 俊夫(三菱電機) |
| 委員長氏名（英） | Toshio Wakayama(Mitsubishi Electric) |
| 副委員長氏名（和） | 網嶋 武(明大) / 毛塚 敦(電子航法研) |
| 副委員長氏名（英） | Takeshi Amishima(Meiji Univ.) / Atsushi Kezuka(ENRI) |
| 幹事氏名（和） | 北村 尭之(三菱電機) / 尚 方(電通大) |
| 幹事氏名（英） | Takayuki Kitamura(Mitsubishi Electric) / Shang Fang(Univ. of Electro-Comm) |
| 幹事補佐氏名（和） | 長縄 潤一(電子航法研) / 赤嶺 賢彦(防衛省) |
| 幹事補佐氏名（英） | Junichi Naganawa(ENRI) / Yoshihiko Akamine(TRDI) |
| 講演論文情報詳細 | |
| 申込み研究会 | Technical Committee on Space, Aeronautical and Navigational Electronics |
|---|---|
| 本文の言語 | ENG |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Towards Satellite Data Fusion For Active Fire Detection |
| サブタイトル（和） | |
| キーワード(1)（和/英） | Kalman Filter / Kalman Filter |
| キーワード(2)（和/英） | Data Fusion / Data Fusion |
| キーワード(3)（和/英） | Wildfire Detection / Wildfire Detection |
| キーワード(4)（和/英） | Earth Observation / Earth Observation |
| キーワード(5)（和/英） | Brightness Temperature / Brightness Temperature |
| キーワード(6)（和/英） | Middle Infrared / Middle Infrared |
| キーワード(7)（和/英） | Diurnal Temperature Cycle / Diurnal Temperature Cycle |
| 第 1 著者 氏名（和/英） | Nur Fajar Trihantoro / Nur Fajar Trihantoro |
| 第 1 著者 所属（和/英） | RMIT University(略称：RMIT大) RMIT University(略称：RMIT Univ.) |
| 第 2 著者 氏名（和/英） | Simon Jones / Simon Jones |
| 第 2 著者 所属（和/英） | RMIT University(略称：RMIT大) RMIT University(略称：RMIT Univ.) |
| 第 3 著者 氏名（和/英） | Karin Reinke / Karin Reinke |
| 第 3 著者 所属（和/英） | RMIT University(略称：RMIT大) RMIT University(略称：RMIT Univ.) |
| 発表年月日 | 2023-12-08 |
| 資料番号 | SANE2023-65 |
| 巻番号（vol） | vol.123 |
| 号番号（no） | SANE-298 |
| ページ範囲 | pp.35-35(SANE), |
| ページ数 | 1 |
| 発行日 | 2023-11-30 (SANE) |