National Taiwan University of Sience and Techlogy | Taiwan
Pei-Jun Lee (IET Fellow, IEEE Senior Member) is a distinguished researcher whose work spans advanced video compression, FPGA-based edge AI computing, and remote sensing satellite imaging systems. Her research portfolio includes over 100 publications, multiple patents, and extensive industry–academia collaborations. She has led major technological innovations in AI-driven satellite imaging circuits, infrared sensor systems, and high-performance embedded architectures. Her work with the Taiwan Space Organization (TASA) on the Formosat-8 mission enabled the development of FPGA circuits for real-time moving object detection and onboard video compression, marking the first Taiwanese satellite with such capabilities. She also contributed to the CubeSat Key Technology R&D Project, resulting in the successful launch of Lilium-I, Taiwan’s first 3U CubeSat equipped with an AI remote sensing payload. Her research further includes optical and mechanical circuit design for infrared imaging modules, 2D/3D conversion systems, multi-view and stereoscopic display technologies, and low-complexity solutions for standards such as 3D-HEVC, H.264, and MPEG-4. Her applied innovations extend to patient-care imaging systems and bio-inspired robotic fish. Through sustained industry collaboration and competitive project achievements, her work demonstrates both strong theoretical innovation and significant impact on practical, space-grade imaging technologies.
Profile: Scopus | Orcid
Featured Publications
Kusumoseniarto, R. H., Lin, Z.-Y., Su, S.-F., & Lee, P.-J. (2025).
Real-time human action recognition with dynamical frame processing via modified ConvLSTM and BERT. IEEE Access.
Bui, T.-A., Lee, P.-J., Liobe, J., Barzdenas, V., & Udris, D. (2025).
Region of interest-focused dynamic enhancement (RoIDE) for satellite images. IEEE Transactions on Geoscience and Remote Sensing.
Hsu, C.-H., Lee, P.-J., & Bui, T.-A. (2025, March 29).
Lightweight feature-enhanced U-Net for landslide change detection in remote sensing imagery.
In Proceedings of the ICCT-Pacific 2025.
Chen, C.-F., Lee, P.-J., & Bui, T.-A. (2025, January 11).
Low parameters UNet for energy-efficient cloud detection.
In 2025 IEEE ICCE Conference Proceedings.
Su, R.-Y., & Lee, P.-J. (2025, January 11).
Tiny objects classification on remote sensing image by using multi-scale crop.
In 2025 IEEE ICCE Conference Proceedings.