TY - GEN
T1 - Quantum Deep Hyperspectral Satellite Remote Sensing
AU - Lin, Chia Hsiang
AU - Chen, You Yao
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Considering that there are many NP-hard problems in remote sensing (e.g., Craig simplex computation in hyperspectral un-mixing), it is natural to introduce quantum computing into space remote sensing. However, current quantum computers have some practical limitations, requiring complementary techniques to support quantum computing. Specifically, we are introducing artificial intelligence (AI) to solve the quantum collapse effect and the phenomenon of insufficient quantum bits (qubits). We thereby propose the hyperspectral quantum deep network (HyperQUEEN) for satellite remote sensing. HyperQUEEN is the first quantum technology that successfully outputs a complete hyperspectral image, given the very limited qubit resources. Existing quantum image processing methods can only achieve classification-level tasks or simple geometry transforms, and are far from being applicable to advanced satellite missions like restoration of damaged hyperspectral images, which HyperQUEEN has successfully achieved for the first time. Remarkable computational efficiency and restoration performances achieved by the radically new quantum AI system - HyperQUEEN - will be reported.
AB - Considering that there are many NP-hard problems in remote sensing (e.g., Craig simplex computation in hyperspectral un-mixing), it is natural to introduce quantum computing into space remote sensing. However, current quantum computers have some practical limitations, requiring complementary techniques to support quantum computing. Specifically, we are introducing artificial intelligence (AI) to solve the quantum collapse effect and the phenomenon of insufficient quantum bits (qubits). We thereby propose the hyperspectral quantum deep network (HyperQUEEN) for satellite remote sensing. HyperQUEEN is the first quantum technology that successfully outputs a complete hyperspectral image, given the very limited qubit resources. Existing quantum image processing methods can only achieve classification-level tasks or simple geometry transforms, and are far from being applicable to advanced satellite missions like restoration of damaged hyperspectral images, which HyperQUEEN has successfully achieved for the first time. Remarkable computational efficiency and restoration performances achieved by the radically new quantum AI system - HyperQUEEN - will be reported.
UR - http://www.scopus.com/inward/record.url?scp=85178327327&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85178327327&partnerID=8YFLogxK
U2 - 10.1109/IGARSS52108.2023.10282472
DO - 10.1109/IGARSS52108.2023.10282472
M3 - Conference contribution
AN - SCOPUS:85178327327
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 7316
EP - 7319
BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Y2 - 16 July 2023 through 21 July 2023
ER -