Adugna Mullissa is a researcher in 'Radar and Deep Learning Methods for near-real time forest change monitoring' at the Laboratory of Geo-information Science and Remote Sensing, Wageningen University (2019-Present). He obtained his PhD (2017) in Microwave Remote Sensing from the University of Twente, Enschede, The Netherlands. Before Joining Wageningen University he was a visiting scientist at Lyle School of Civil Engineering, Purdue University, United States (2016-2017). He was also a researcher at the University of Twente investigating deep learning methodologies for crop classification using polarimetric SAR data (2018-2019).
His research interest is on using microwave remote sensing and machine learning to improve accuracy and efficiency in environmental monitoring with a particular focus on SAR, PolSAR and InSAR signal processing and information extraction using deep neural networks. He worked on state-of-the-art deep learning models for SAR and PolSAR image despeckling, PolSAR image semantic segmentation as well as SAR time-series forecast and segmentattion. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and IEEE Geoscience and Remote Sensing Society. Dr. Mullissa is a Referee for multiple journals, including IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Ssensing Letters and IEEE Journal of Selected Topics in Applied Eearth Observations and Remote Sensing.
His list of publications can be found in Google Scholar and ResearchGate.