Image Fusion with Quantum Processing for Remote Sensing

Tutorial Presenters: Leslie Miller, SenSIP Graduate Research Associate, ECEE
Andreas Spanias, Professor and SenSIP Center Director

Abstract: The integration of Synthetic Aperture Radar (SAR) and optical imagery through image fusion techniques is important in refining the accuracy and interpretability of images in remote sensing applications. The current array of available image fusion methods presents a complex landscape, each with its own set of advantages and limitations. This Tutorial will cover existing image fusion methods, with a primary focus on SAR and optical image classification. Furthermore, the tutorial will provide valuable insights for enhanced decision-making in remote sensing applications. In addition to examining image fusion methods, we will also present a study on various machine learning algorithms that can be used to classify the data. Although the emphasis is on SAR and optical image fusion, the study will also include a broader discussion of other applications, of image fusion methods across various domains.  The tutorial is also accompanied by a survey paper that will be included in the proceedings of the conference.

The work presented at this conference is sponsored in part by the SenSIP center, the NSF IRES award 1854273, and the  Quantum Collaborative.

Leslie Miller is a PhD student in Electrical Engineering and a research associate with the SenSIP Center at Arizona State University. Her research interests are in signal processing, communication systems, and quantum computing. Leslie began her work in the SenSIP center laboratory in the summer of 2022. She has been an active graduate research associate and an NSF REU and NSF IRES recipient focused on exploring quantum computing algorithms for classifying remote sensing data. In addition to her work with the SenSIP center, she has completed three internships as a civilian engineer with the DoD.  She also served as the President for the IEEE – Eta Kappa Chapter in 2023. She received the ASU Fulton Impact Award and was the convocation speaker for the ASU Fulton Schools in May 2023.  Her quantum research was published at the IEEE Aerospace Conference and the IEEE DSP conference.  Additionally, she has co-authored two provisional patents.

Andreas Spanias is a Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University (ASU). He is also the Director of the Sensor Signal and Information Processing (SenSIP) Center and the founder of the SenSIP industry consortium (previously an NSF I/UCRC site). His research interests are in the areas of adaptive signal processing, speech processing, quantum machine learning, and sensor systems. He is the author of two textbooks: Audio Processing and Coding by Wiley and DSP; An Interactive Approach (2nd Ed.). He has contributed to more than 350 papers, 11 monographs, 26 full patents, and several provisional patents. He served as Associate Editor of the IEEE Transactions on Signal Processing and as the General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing Vice-President for Conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as a Distinguished Lecturer for the IEEE Signal Processing Society. He received the 2018 IEEE Region 6 Outstanding Educator Award (across 12 states). In 2018, he was elected as a Senior Member of the National Academy of Inventors (NAI).