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Título: A new methodology for assessing SAR despeckling filters
Autores/as: Vasquez Salazar, Ruben Dario 
Cardona Mesa, Ahmed Alejandro 
Gómez Déniz, Luis 
Travieso González, Carlos Manuel 
Clasificación UNESCO: 220920 Radiometría
Palabras clave: Deep Learning (Dl)
Multitemporal Fusion
Noise Measurement
Optical Filters
Protocols, et al.
Fecha de publicación: 2024
Publicación seriada: IEEE Geoscience and Remote Sensing Letters 
Resumen: Deep Learning methods require immense amounts of labeled data to provide reasonable results. In computer vision applications, and more specifically in despeckling SAR (Synthetic Aperture Radar) images, due to the speckle content, there is no ground truth available. To test the performances of despeckling filters, the common protocol is to synthetically corrupt optical images with a suitable speckle model and then, after filtering, well-known metrics are obtained. Then, filters are tested on actual SAR data. However, even the most elaborated speckle models are far from accounting for the complex mechanisms related to SAR images. In this paper, a methodology to design a realistic dataset is proposed. Actual SAR images of the same scene acquired with the same sensor on different dates, then they are properly co-registered and averaged to get a ground truth-like reference image to objectively evaluate the performance of a despeckling method. To show the benefits of the proposed methodology, a deep learning approach is used to filter the data by using the designed dataset, which will be called the “SAR model”. Then they are compared with the standard protocol by using synthetically corrupted optical images, which will be the “Synthetic model”. One last validation is performed by filtering the same images with FANS, a well-known despeckling filter and compared with the results obtained with autoencoder. The validation on actual SAR data not included in the training phase validates the proposed methodology. From the results shown, it is recommended to test filters on the proposed more realistic dataset.
ISSN: 1545-598X
DOI: 10.1109/LGRS.2024.3357211
Fuente: IEEE Geoscience and Remote Sensing Letters [ISSN 1545-598X], (Enero 2024)
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