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アイテム
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
https://doi.org/10.18997/00009002
https://doi.org/10.18997/000090022ad0aa58-3d24-4f91-a3f2-f7d60ca21d11
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
kou_k_556.pdf (5.7 MB)
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| アイテムタイプ | 学位論文 = Thesis or Dissertation(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2022-11-24 | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||||
| 資源タイプ | doctoral thesis | |||||||
| タイトル | ||||||||
| タイトル | Pre- and post-processing of space imaging for wildfire detection using convolution neural network approach | |||||||
| 言語 | en | |||||||
| タイトル | ||||||||
| 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 | |||||||
| 言語 | ja | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 著者 |
Muhammad Hasif bin Azami
× Muhammad Hasif bin Azami
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | 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. | |||||||
| 言語 | en | |||||||
| 目次 | ||||||||
| 内容記述タイプ | TableOfContents | |||||||
| 内容記述 | 1: Introduction||2: Research Background and Literature Reviews||3: Research Methodology||4: Results||5: Discussion||6: Conclusion and Recommendation | |||||||
| 備考 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 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 | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | wildfire | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | convolution neural network | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | onboard classification | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | optical payload | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | CubeSat | |||||||
| アドバイザー | ||||||||
| 趙, 孟佑 | ||||||||
| 学位授与番号 | ||||||||
| 学位授与番号 | 甲第556号 | |||||||
| 学位名 | ||||||||
| 学位名 | 博士（工学） | |||||||
| 学位授与年月日 | ||||||||
| 学位授与年月日 | 2022-09-26 | |||||||
| 学位授与機関 | ||||||||
| 学位授与機関識別子Scheme | kakenhi | |||||||
| 学位授与機関識別子 | 17104 | |||||||
| 学位授与機関名 | 九州工業大学 | |||||||
| 学位授与年度 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 令和4年度 | |||||||
| 出版タイプ | ||||||||
| 出版タイプ | VoR | |||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||
| ID登録 | ||||||||
| ID登録 | 10.18997/00009002 | |||||||
| ID登録タイプ | JaLC | |||||||
