Chairs: Prof. Mihai Dutcu (DLR), Assoc. Prof. Zhongling Huang (Northwestern Polytechnical University), Prof. Zhe Zhang (AIR, CAS)

Abstract:

In radar imaging, processing and image interpretation fields, the past traditional approaches based on scientific theories have developed for years. Recently, the rapid advancement of artificial intelligence technologies and the growing accessibility of radar data have generated significant interest in the field of machine learning and deep neural networks. The prevailing methodologies mostly revolve around the utilization of powerful deep learning algorithms in the field of computer vision, which have demonstrated notable success. Nonetheless, it continues to confront the obstacles of limited annotated data, inconsistencies in the physical predictions, and a dearth of interpretability.

Theory-guided data science aims to investigate the realm of knowledge discovery and pattern recognition by effectively utilizing the existing data while remaining cognizant of the fundamental scientific knowledge. This session calls for papers aiming to ground novel knowledge guided AI approaches, which integrate the domain knowledge, electromagnetic theory, physical models of radar with deep neural networks, for radar image processing and interpretation.

The broad topics of interest:

l  Cascade or fusion of physical model and DNNs

l  Mutual substitution of DNN and physical mode

l  Physical model informed interpretable deep neural network

l  Radar knowledge guided model initialization and transfer learning

l  Radar knowledge inspired regularization

l  Theoretical constraints for model optimization

l  Radar knowledge guided design of model architecture

l  Deep learning aided 2D/3D/4D radar imaging

The applications include (but are not limited to):

l  Target detection, tracking, and recognition

l  Radar image simulation and generation

l  PolSAR image classification

l  2D/3D SAR imaging

l  SAR imaging semantic segmentation and change detection

l  3D/4D automative radar imaging and applications

l  Radar target characterizing

l  Artificial intelligent oceanography