| Description: |
九州工業大学 ; 博士（工学） ; 1: Introduction||2: Research Background and Literature Reviews||3: Research Methodology||4: Results||5: Discussion||6: Conclusion and Recommendation ; 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. ... |