101 citations · 133 across the 5 of their papers we have counts for
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Sentinel-1 SAR Based Weakly Supervised Learning For Tropical Forest Mapping
Adugna Mullissa, Sassan Saatchi
Tropical forests play an important role in regulating the global carbon cycle and are crucial for maintaining the tropical forest biodiversity. Therefore, there is an urgent need t…
Despeckling Polarimetric SAR Data Using a Multi-Stream Complex-Valued Fully Convolutional Network
Adugna G. Mullissa, Claudio Persello, Johannes Reiche
A Polarimetric Synthetic Aperture Radar (PolSAR) sensor is able to collect images in different polarization states, making it a rich source of information for target characterizati…
deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling
Adugna G. Mullissa, Diego Marcos, Devis Tuia +2
Deep learning (DL) has proven to be a suitable approach for despeckling synthetic aperture radar (SAR) images. So far, most DL models are trained to reduce speckle that follows a p…