Paper 13039-32
Paper 13039-32
Remote sensing and vision problems under adverse imaging conditions (Invited Paper)
23 April 2024 • 11:10 AM - 11:50 AM EDT | National Harbor 5
Abstract
Remotes sensing and vision problems like object detection and recognition from various active and passive sensors are of great value to many DoD use cases. Usually due to sensor or communication link limitations the images received are of low resolution, quality and have compression artifacts. To combat this we developed a new direct vision task feature pyramid recovery with a joint frequency and pixel domain neural learning approach. It had many successes in problems like ATR from low resolution SAR and EO images, joint delburring and target detection, as well as the very low bit rate complex SAR image compression for phase recovery.
Presenter
Zhu Li
Univ. of Missouri-Kansas City (United States)
Zhu Li is a professor of ECE with the University of Missouri, Kansas City, he directs the NSF I/UCRC Center for Big Learning at UMKC. He received his PhD in Electrical & Computer Engineering from Northwestern University in 2004, and was the AFRL Summer Faculty at the US Air Force Academy, UAV Research Center, 2016-18, 2022-24. He is Associate EiC for IEEE TCSVT, AE for IEEE T-IP. His research interests include remote sensing, imaging and vision, image processing and compression, and associated optimization and machine learning problems. He has 50+ issued or pending patents, 200+ publications in book chapters, journals, conference proceedings and standards contributions in these areas. He received a Best Paper Runner Up award at CVPR PBVS 2023, Best Paper Award from IEEE Int'l Conf on Multimedia & Expo (ICME) at Toronto, 2006, and a Best Paper Award from IEEE Int'l Conf on Image Processing (ICIP) at San Antonio, 2007.