A New Methodology for Assessing SAR Despeckling Filters

Deep-learning (DL) methods require immense amounts of labeled data to provide reasonable results. In computer vision applications, and more specifically in despeckling synthetic aperture radar (SAR) images, due to the speckle content, there is no ground truth available. To test the performances of d...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters 2024, Vol.21, p.1-5
Hauptverfasser:	Vasquez-Salazar, Ruben Dario, Cardona-Mesa, Ahmed Alejandro, Gomez, Luis, Travieso-Gonzalez, Carlos M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer vision Datasets Deep learning (DL) Filters Image acquisition Image filters Methodology Methods multitemporal fusion Noise measurement Optical filters Protocols Radar imaging Radar polarimetry SAR (radar) Speckle speckle noise supervised learning Synthetic aperture radar synthetic aperture radar (SAR) data Training
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