TY - GEN
T1 - Wildfire segmentation analysis from edge computing for on-board real-time alerts using hyperspectral imagery
AU - Spiller, Dario
AU - Thangavel, Kathiravan
AU - Sasidharan, Sarathchandrakumar T.
AU - Amici, Stefania
AU - Ansalone, Luigi
AU - Sabatini, Roberto
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper investigates the opportunity to use artificial intelligence methodologies and edge computing approaches for wildfire detection directly from satellite platforms. The test case for our study is PRISMA (Precursore IperSpettrale della Missione Applicativa-Hyperspectral Precursor of the Application Mission), the Italian hyperspectral satellite launched in 2019 by the Italian Space Agency. This mission provides hyperspectral (HS) images in the spectral range of [0.4,2.5] μm and an average spectral resolution less than 10 nm. This work reports new results related to the Australian bushfires happened in December 2019 in New South Wales, captured by PRISMA on December 27, 2019. Starting from a one-dimensional convolutional neural network (CNN) discussed in previous authors' works to perform multiclass classification, this paper primarily deals with the opportunity to use hardware accelerators, namely the Intel Movidius Myriad 2, the Nvidia Jetson TX2, and the Nvidia Jetson Nano, to consider the on-the-edge implementation of the CNN. This study is in line with the current impulse to improve on-board computing capabilities and platform autonomy, setting some of the elements for future satellites or constellations focusing on specific remote sensing tasks to provide real-time reliable early warnings.
AB - This paper investigates the opportunity to use artificial intelligence methodologies and edge computing approaches for wildfire detection directly from satellite platforms. The test case for our study is PRISMA (Precursore IperSpettrale della Missione Applicativa-Hyperspectral Precursor of the Application Mission), the Italian hyperspectral satellite launched in 2019 by the Italian Space Agency. This mission provides hyperspectral (HS) images in the spectral range of [0.4,2.5] μm and an average spectral resolution less than 10 nm. This work reports new results related to the Australian bushfires happened in December 2019 in New South Wales, captured by PRISMA on December 27, 2019. Starting from a one-dimensional convolutional neural network (CNN) discussed in previous authors' works to perform multiclass classification, this paper primarily deals with the opportunity to use hardware accelerators, namely the Intel Movidius Myriad 2, the Nvidia Jetson TX2, and the Nvidia Jetson Nano, to consider the on-the-edge implementation of the CNN. This study is in line with the current impulse to improve on-board computing capabilities and platform autonomy, setting some of the elements for future satellites or constellations focusing on specific remote sensing tasks to provide real-time reliable early warnings.
KW - hyperspetral imagery
KW - on-the-edge computing
KW - PRISMA
KW - Wildfire detection
UR - http://www.scopus.com/inward/record.url?scp=85144605918&partnerID=8YFLogxK
U2 - 10.1109/MetroXRAINE54828.2022.9967553
DO - 10.1109/MetroXRAINE54828.2022.9967553
M3 - Conference contribution
AN - SCOPUS:85144605918
T3 - 2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings
SP - 725
EP - 730
BT - 2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022
Y2 - 26 October 2022 through 28 October 2022
ER -