Adugna Mullissa
Citado por
Citado por
Sentinel-1 sar backscatter analysis ready data preparation in google earth engine
A Mullissa, A Vollrath, C Odongo-Braun, B Slagter, J Balling, Y Gou, ...
Remote Sensing 13 (10), 1954, 2021
Forest disturbance alerts for the Congo Basin using Sentinel-1
J Reiche, A Mullissa, B Slagter, Y Gou, T Nandin-Erdene, ...
Environmental Research Letters 16 (2), 12, 2021
Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine
A Vollrath, A Mullissa, J Reiche
Remote Sensing 12 (11), 1-14, 2020
Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series
RN Masolele, V De Sy, M Herold, D Marcos, J Verbesselt, F Gieseke, ...
Remote Sensing of Environment 264, 112600, 2021
PolSARNet: A Deep Fully Convolutional Network for Polarimetric SAR Image Classification
AG Mullissa, C Persello, A Stein
IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2019
deSpeckNet: Generalizing Deep Learning-Based SAR Image Despeckling
AG Mullissa, D Marcos, D Tuia, M Herold, J Reiche
IEEE Transactions on Geoscience and Remote Sensing, 2020
Fully Convolutional Networks for Multi-temporal SAR Image classification
Mullissa, A.G., Persello, C., Tolpekin, V.A.
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 6639-6642, 2018
Polarimetry-Based Distributed Scatterer Processing Method for PSI Applications
AG Mullissa, D Perissin, V Tolpekin, A Stein
IEEE Transactions on Geoscience and Remote Sensing 56 (6), 3371-3382, 2018
Polarimetric Differential SAR Interferometry in an Arid Natural Environment
AG Mullissa, VA Tolpekin, A Stein, D Perissin
International Journal of Applied Earth Observations and Geoinformation 59, 9-18, 2017
Despeckling Polarimetric SAR Data Using a Multistream Complex-Valued Fully Convolutional Network
AG Mullissa, C Persello, J Reiche
IEEE Geoscience and Remote Sensing Letters, 2021
Scattering property based contextual PolSAR speckle filter
A Mullissa, A.G., Tolpekin, V.A., Stein
International Journal of Applied Earth Observation and Geoinformation 63 …, 2017
Monitoring direct drivers of small-scale tropical forest disturbance in near real-time with Sentinel-1 and-2 data
B Slagter, J Reiche, D Marcos, A Mullissa, E Lossou, M Peña-Claros, ...
Remote Sensing of Environment 295, 113655, 2023
Angular-based radiometric slope correction for Sentinel-1 on google earth engine, Remote Sens., 12, 1867
A Vollrath, A Mullissa, J Reiche
Intra-annual relationship between precipitation and forest disturbance in the African rainforest
Y Gou, J Balling, V De Sy, M Herold, W De Keersmaecker, B Slagter, ...
Environmental Research Letters 17 (4), 044044, 2022
Deep learning and automatic reference label harvesting for Sentinel-1 SAR-based rapid tropical dry forest disturbance mapping
A Mullissa, J Reiche, M Herold
Remote Sensing of Environment 298, 113799, 2023
The Amazon’s 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction
FH Wagner, S Favrichon, R Dalagnol, M Hirye, A Mullissa, S Saatchi
Remote Sensing 16 (6), 1056, 2024
Seasonal Forest Disturbance Detection Using Sentinel-1 SAR & Sentinel-2 Optical Timeseries Data and Transformers
A Mullissa, J Reiche, S Saatchi
IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium …, 2023
LUCA: A Sentinel-1 SAR-Based Global Forest Land Use Change Alert
A Mullissa, S Saatchi, R Dalagnol, T Erickson, N Provost, F Osborn, ...
Remote Sensing 16 (12), 2151, 2024
Beyond assimilation of leaf area index: Leveraging additional spectral information using machine learning for site-specific soybean yield prediction
DV Gaso, D Paudel, A de Wit, LA Puntel, A Mullissa, L Kooistra
Agricultural and Forest Meteorology 351, 110022, 2024
Critical Assessment of Cocoa Classification with Limited Reference Data: A Study in Côte d’Ivoire and Ghana Using Sentinel-2 and Random Forest Model
N Moraiti, A Mullissa, E Rahn, M Sassen, J Reiche
Remote Sensing 16 (3), 598, 2024
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