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Abstract: Deep Learning-Based Radio Frequency Interference Detection for SMAP Radiometry Using Semi-Synthetic Time-Frequency Representations (AGU25)

Deep Learning-Based Radio Frequency Interference Detection for SMAP Radiometry Using Semi-Synthetic Time-Frequency Representations

Ahmed Manavi Alam1, Mehmet Kurum2 and Ali Gurbuz1, (1)North Carolina State University, Electrical and Computer Engineering, Raleigh, United States, (2)University of Georgia, Electrical and Computer Engineering, Athens, United States
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