Data fusion has become a popular multidisciplinary approach that combines data from multiple sources to improve the global potential value and interpretation performance and produce a high-quality final representation of the data. Fusion techniques are useful for various applications like object detection, recognition, identification and classification, object tracking, change detection, decision making, etc. And a remarkable improvement over conventional probabilistic data fusion techniques is coming out of machine learning (ML) techniques, which include strong computing and predicting abilities. Despite the fast development, data fusion approaches remain challenging for remote sensing applications due to various requirements, landscape complexity, temporal, spatial, and spectral variations, etc. within the input dataset.
This special issue focuses on intelligent and advanced signal processing techniques to overcome the DF challenges in ML-based remote sensing applications. Indeed, advanced signal processing solutions can be researched to meet DF challenges for RS applications especially when ML techniques are applied. Both theoretical and experimental studies are welcome, particularly papers with good technical insights on the intelligent and advanced signal processing techniques for RS applications.
Lead Guest Editor:
Silvia Liberata Ullo, University of Sannio, Benevento, Italy
Parameshachari B.D. (Bidare Divakarachari), Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India
Issaak Parcharidis, Harokopio University, Athens, Greece