Publications
A. Mullissa, S. Saatchi, R. Dalagnol, T. Erickson, N. Provost, F. Osborn, A. Ashary, V. Moon, D. Melling, “LUCA: A Sentinel-1 SAR-Based Global Forest Land Use Change Alert” Remote Sensing 16 (12) (2024) 2151 [full text]
N. Moraiti, A. Mullissa, E. Rahn, M. Sassen, J. Reiche, “Critical Assessment of Cocoa Classification with Limited Reference Data: A Study in Côte d’Ivoire and Ghana Using Sentinel-2 and Random Forest Model” Remote Sensing 16 (3) (2024) 598 [full text]
F. Wagner, S. Favrichon, R. Dalagnol, M. Hirye, A.Mullissa, S. Saatchi, “The Amazon’s 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction” Remote Sensing 16 (6) (2024) 1056 [full text]
A. Mullissa, J. Reiche, M. Herold, “Deep learning and automatic reference label harvesting for Sentinel-1 SAR-based rapid tropical dry forest disturbance mapping” Remote Sensing of Environment, 298, 113799, December 2023. [full text]
A. Mullissa, C. Odongo-Braun, B. Slagter, J. Balling,Y. Gou, N. Gorelick, J. Reiche, “Sentinel-1 SAR backscatter analysis ready data preparation in google earth engine” Remote Sensing, vol. 10, no. 13, pp. 1953, May 2021. [full text] [code]
A. Mullissa, C. Persello and J. Reiche, “Despeckling Polarimetric SAR Data Using a Multistream Complex-Valued Fully Convolutional Network” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, March 2022. [full text] [code]
Yaqing, G., Balling, J., De Sy, N., Herold, M., De Keersmaecker, W., Slagter, B., Mullissa, A., Shang, X, and J. Reiche, “Intra-annual relationship between precipitation and forest disturbance in the African rainforest” Environmental Research Letters, vol. 17, no. 4, March 2022. [full text]
J. Reiche, A. Mullissa, B. Slagter, Y. Gou, N.-E. Tsendbazar, C. Odongo-Braun, A. Vollrath, M. J. Weisse, F. Stolle, A. Pickens, et al., “Forest disturbance alerts for the congo basin using sentinel-1” Environmental Research Letters 16 (2) (2021) 024005 [full text]
Slagter, B., Reiche, J., Marcos, D., Mullissa, A., Lossou, E., Peña-Claros, M., Herold, M. Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series. Remote Sensing of Environment, 295, (2023), [full text]
Masolele, R., De Sy, V., Herold, M., Marcos, D., Verbesselt, J., Gieske, F., Mullissa, A., Martius, C. Monitoring direct drivers of small-scale tropical forest disturbance in near real-time with sentinel-1 and -2 data. Remote Sensing of Environment, 264, (2021), [full text]
A. Mullissa, D. Marcos, D. Tuia, M. Herold and J. Reiche, “deSpeckNet: Generalizing Deep Learning-Based SAR Image Despeckling” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1-15, 2022 [full text] [MATLAB code] [GEE code]
A. Vollrath, A. Mullissa, J. Reiche, “Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine” Remote Sensing 2020, 12, 1867. [full text]
A. Mullissa, D. Perissin, V. A. Tolpekin and A. Stein, “Polarimetry-Based Distributed Scatterer Processing Method for PSI Applications” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 6, pp. 3371-3382 [full text]
A. Mullissa, C. Persello and A. Stein, “PolSARNet: A Deep Fully Convolutional Network for Polarimetric SAR Image Classification” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [full text]
A. Mullissa, “Quality aspects of distributed scatterers in polarimetric differential SAR interferometry” University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC) Decenber 2017 [full text]
A. Mullissa, V. Tolpekin, and A. Stein, “Scattering property based contextual PolSAR speckle filter” Int. J. Appl. Earth Observ. Geoinf., vol. 63, pp. 78–89, Dec. 2017 [full text]
A. Mullissa, V. Tolpekin, A. Stein, and D. Perissin “Polarimetric differential SAR interferometry in an arid natural environment” Int. J. Appl. Earth Observ. Geoinf., vol. 59, pp. 9–18, Jul. 2017 [full text]
Gaso, D. V., Paudel, D., de Wit, A., Puntel, L. A., Mullissa, A., & Kooistra, L. (2024). Beyond assimilation of Leaf Area index: Leveraging additional spectral information using machine learning for site-specific soybean yield prediction. Agricultural and Forest Meteorology, 351, 110022. https://doi.org/10.1016/j.agrformet.2024.110022
A. Mullissa, C. Persello, V. Tolpekin “Fully Convolutional Networks For Multi-temporal SAR Image Classification” IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE,[full text]
A. Mullissa, D. Marcos, M. Herold, J. Reiche “Dual Polarimetric SAR Covariance Matrix Estimation Using Deep Learning” IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, IEEE,[full text]
A. Mullissa, J. Reiche, S. Saatchi “Seasonal Forest Disturbance Detection Using Sentinel-1 SAR & Sentinel-2 Optical Timeseries Data and Transformers” IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, IEEE,[full text]
A. Mullissa, and S. Saatchi, “Sentinel-1 SAR Based Weakly Supervised Learning for Tropical Forest Mapping,” IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Athens, Greece, 2024, pp. 2191-2195, doi: 10.1109/IGARSS53475.2024.10642847.[full text]
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