Quantum Deep Hyperspectral Satellite Remote Sensing

Chia Hsiang Lin, You Yao Chen

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面7316-7319
頁數4
ISBN(電子)9798350320107
DOIs
出版狀態Published - 2023
事件2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States
持續時間: 2023 7月 162023 7月 21

出版系列

名字International Geoscience and Remote Sensing Symposium (IGARSS)
2023-July

Conference

Conference2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
國家/地區United States
城市Pasadena
期間23-07-1623-07-21

All Science Journal Classification (ASJC) codes

  • 電腦科學應用
  • 一般地球與行星科學

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