Onboard AI for Fire Smoke Detection Using Hyperspectral Imagery: An Emulation for the Upcoming Kanyini Hyperscout-2 Mission

This article presents our research in the prelaunch phase of the Kanyini mission, which aims to implement an energy-efficient, AI-based system onboard for early fire smoke detection using hyperspectral imagery. Our approach includes three key components: developing a diverse hyperspectral training d...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.9629-9640
Main Authors: Lu, Sha, Jones, Eriita, Zhao, Liang, Sun, Yu, Qin, Kai, Liu, Jixue, Li, Jiuyong, Abeysekara, Prabath, Mueller, Norman, Oliver, Simon, O'Hehir, Jim, Peters, Stefan
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial intelligence (AI) onboard
Artificial neural networks
Computational modeling
Emulation
Energy consumption
Energy efficiency
fire smoke detection
Fires
Hyperspectral imaging
Imagery
Neural networks
Onboard
satellite emulation
Satellites
Smoke
Task analysis
Training
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Summary:This article presents our research in the prelaunch phase of the Kanyini mission, which aims to implement an energy-efficient, AI-based system onboard for early fire smoke detection using hyperspectral imagery. Our approach includes three key components: developing a diverse hyperspectral training dataset from VIIRS imagery, groundwork in band selection and AI model preparation, and developing an emulation system. We adapted and evaluated our previously developed lightweight convolutional neural network model, VIB_SD, to meet the computational constraints of satellite deployment. The emulation system tests various onboard AI tasks and processes. Our comprehensive experiments demonstrate the feasibility and benefits of employing onboard AI for fire smoke detection, significantly improving downlink efficiency, energy consumption, and detection speed.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2024.3394574