畳み込みニューラルネットワークアプローチを使用した山火事検出のための宇宙イメージングの前処理と後処理

• 資料種別: 博士論文
• タイトル: 畳み込みニューラルネットワークアプローチを使用した山火事検出のための宇宙イメージングの前処理と後処理
• 著者・編者: Muhammad Hasif bin Azami
• 著者標目: Muhammad Hasif bin Azami
• 出版年月日等: 2022-09-26
• 出版年（W3CDTF）: 2022-09-26
• 並列タイトル等: Pre- and post-processing of space imaging for wildfire detection using convolution neural network approach
• 授与機関名: 九州工業大学
• 授与年月日: 2022-09-26
• 授与年月日（W3CDTF）: 2022-09-26
• 報告番号: 甲第556号
• 学位: 博士（工学）
• 本文の言語コード: eng
• 件名標目: wildfire; convolution neural network; onboard classification; optical payload; CubeSat
• 対象利用者: 一般
• 一般注記: 九州工業大学博士学位論文 学位記番号：工博甲第556号 学位授与年月日：令和4年9月26日; 令和4年度
• DOI: 10.18997/00009002; https://doi.org/10.18997/00009002
• 国立国会図書館永続的識別子: info:ndljp/pid/12664835; https://dl.ndl.go.jp/pid/12664835
• コレクション（共通）: 障害者向け資料
• コレクション（障害者向け資料：レベル1）: テキストデータ
• コレクション（個別）: 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文; https://dl.ndl.go.jp/collections/A00014
• 収集根拠: 博士論文（自動収集）
• 受理日（W3CDTF）: 2023-03-03T17:22:32+09:00
• 記録形式（IMT）: application/pdf
• オンライン閲覧公開範囲: 国立国会図書館内限定公開
• デジタル化資料送信: 図書館・個人送信対象外
• 遠隔複写可否（NDL）: 可
• 掲載誌（URI）: https://doi.org/10.18997/00009002; http://hdl.handle.net/10228/00009002
• 連携機関・データベース: 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp

• 要約等: An increasing number of wildfire cases every year has caused fear around the world. Scientists and researchers agreed that this catastrophe occurred due to climate change. Dry and windy conditions had worsened the situation in the affected area. Properties and life losses have created serious concerns for the authority to find a solution for preparing and fighting the fire promptly. Since the late ‘70s, leveraging satellite technology has brought helpful insight to monitor, detect, and assess wildfire events. NOAA AVHRR is one of the oldest Earth Observation （EO） satellites with the main objective of detecting and mapping forest fires. The MODIS fire product regularly upgrades the sensor technology and launches the satellites into space. However, with the advancement of current technologies, a miniaturized satellite called CubeSat creates a novel mission design by reducing the satellite development time, increasing the launching batch in a constellation method, and enhancing the detection result wildfire. The prime limitations of CubeSat are the size, weight, and power （SWaP）, which lead to the optimization design of the payload and the communication subsystem. The big image data acquired by the CubeSat creates a bottleneck effect between the satellite and the ground station due to the low downlink data rate. Deep learning （DL） techniques are improving in the computer vision area. Image classification, detection, and segmentation are used in neural network architecture designed by artificial intelligence researchers. In this study, the convolution neural network （CNN） algorithm was chosen for the pre-processing onboard CubeSat for wildfire detection as well as for the graphical user interface （GUI） used on the ground post-processing. The first and crucial step was to develop a custom dataset for wildfire images by leveraging satellite imagery. Defining the specifications of the CubeSat payload to which the CNN was implemented could support selecting the accurate resolution and bands for acquiring the satellite images. The KITSUNE satellite is a 6-unit CubeSat platform implementing the CNN onboard for wildfire image classification. It serves as the secondary mission to support the main mission of a 5-m class EO. The on-ground testing revealed that the CNN could classify wildfire occurrences on the satellite system using the MiniVGGNet network with an overall accuracy of 98 % and an F1-score of 97% success rate in 137 seconds. Other models were also compared, such as ResNet and MiniGoogLeNet implemented on the GUI with 97% and 96% F1-score, respectively. Overall, this research showed the feasibility of CubeSat of executing CNN onboard in orbit, particularly for wildfire detection.
• DOI: 10.18997/00009002; https://doi.org/10.18997/00009002
• 記録形式（IMT）: application/pdf
• 一次資料へのリンクURL: kou_k_556.pdf （fulltext）; https://kyutech.repo.nii.ac.jp/record/7799/files/kou_k_556.pdf
• オンライン閲覧公開範囲: インターネット公開
• 連携機関・データベース: 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• 提供元機関・データベース: 九州工業大学 : キューテイカー

目次

2023-08-05 再収集

2023-10-11 再収集