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International Geoscience and Remote Sensing Symposium

In 2021 a joint initiative of Belgium and The Netherlands

TU1.O-16.5

COMPARATIVE EVALUATION OF DEEP LEARNING-BASED SAR-OPTICAL IMAGE MATCHING APPROACHES

Lloyd Hughes, Lloyd Hughes Consulting, South Africa; Michael Schmitt, Munich University of Applied Sciences, Germany
Session:
Deep Learning and SAR Despeckling: An Open and Challenging Issue
Track:
Invited Sessions
Presentation Time:
Tue, 13 Jul, 09:30-09:45 (UTC)
Tue, 13 Jul, 11:30-11:45 Central Europe Summer Time (UTC +2)
Tue, 13 Jul, 17:30-17:45 China Standard Time (UTC +8)
Tue, 13 Jul, 05:30-05:45 Eastern Daylight Time (UTC -4)
Session Co-Chairs:
Florence Tupin, Telecom Paris and Giampaolo Ferraioli, Università degli Studi di Napoli Parthenope
Session Manager:
Axel Deijns
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Session TU1.O-16
TU1.O-16.1: A review of deep-learning techniques for SAR image restoration
Loïc Denis, Université de Lyon, Université Jean-Monnet Saint-Etienne, France; Emanuele Dalsasso, Florence Tupin, Telecom Paris, France
TU1.O-16.3: IMPACT OF TRAINING SET DESIGN IN CNN-BASED SAR IMAGE DESPECKLING
Antonio Mazza, Giuseppe Scarpa, Luisa Verdoliva, Giovanni Poggi, University Federico II, Italy
TU1.O-16.4: A MULTI-OBJECTIVE APPROACH FOR MULTI-CHANNEL SAR DESPECKLING
Sergio Vitale, Università degli Studi di Napoli Parthenope, Italy; Hossein Aghababaei, University of Twente, Netherlands; Giampaolo Ferraioli, Vito Pascazio, Gilda Schirinzi, Università degli Studi di Napoli Parthenope, Italy
TU1.O-16.5: COMPARATIVE EVALUATION OF DEEP LEARNING-BASED SAR-OPTICAL IMAGE MATCHING APPROACHES
Lloyd Hughes, Lloyd Hughes Consulting, South Africa; Michael Schmitt, Munich University of Applied Sciences, Germany
TU1.O-16.6: A COHERENT GENERATIVE SCHEME FOR SAR IMAGE REPRESENTATION
Dong-Xiao Yue, Feng Xu, Fudan University, China

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