Speaker: Shuang Wang

Affiliation: Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Xidian University.

Academic title: Professor

Abstract:

Synthetic aperture radar (SAR) image processing technology based on deep learning has greatly improved the ability of ground observation. It is widely used in land resource monitoring, environmental protection, natural disaster monitoring, etc. SAR image matching is a crucial and essential task for multi-temporal image change information detection, multi-modal information fusion, and multi-perspective image analysis and interpretation. However, it is difficult to obtain a large number of labeled images for deep learning model optimization, which will limit the ability of deep feature representation and degrade SAR image processing performances. Meanwhile, there are various noise interferences and complex imaging mechanisms, increasing the challenge of SAR image processing. This report will introduce research on deep feature learning methods for SAR image matching and processing based on limited labeled samples.

Biography:

Shuang Wang, Senior Member, IEEE. She is a professor at the Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Xidian University. Her research interests include deep learning, intelligent information processing, SAR image processing, etc. Professor Wang has published about 80 papers, and the Google citation is 3000+. Her many research works have been published in journals of IEEE Transactions on Geoscience and Remote Sensing (IEEE TGRS), ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS), IEEE Transactions on Image Processing (IEEE TIP), IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), and CCF-A conferences of IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) and International Conference on Computer Vision (ICCV). Professor Wang also guides students acquired many international competition awards, such as the 2018/2022 IEEE GRSS Data Fusion Contest, the 2022 CVPR Agriculture-vision Cropharvest challenge, and the 2022 IJCAI-ECAI Landslide4Sense competition.