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Title: Labeled dataset for training despeckling filters for SAR imagery
Authors: Vasquez Salazar, Ruben Dario 
Cardona Mesa, Ahmed Alejandro 
Gómez, Luis 
Travieso-González, Carlos M. 
Garavito-González, Andrés F.
Vásquez-Cano, Esteban
Keywords: Image Denoising
Labeled Dataset
Supervised Learning
Synthetic Aperture Radar (Sar)
Issue Date: 2024
Journal: Data in Brief 
Abstract: When training Artificial Intelligence and Deep Learning models, especially by using Supervised Learning techniques, a labeled dataset is required to have an input with data and its corresponding labeled output data. In the case of images, for classification, segmentation, or other processing tasks, a pair of images is required in the same sense, one image as an input (the noisy image) and the desired (the denoised image) one as an output. For SAR despeckling applications, the common approach is to have a set of optical images that then are corrupted with synthetic noise, since there is no ground truth available. The corrupted image is considered the input and the optical one is the noiseless one (ground truth). In this paper, we provide a dataset based on actual SAR images. The ground truth was obtained from SAR images of Sentinel 1 of the same region in different instants of time and then they were processed and merged into one single image that serves as the output of the dataset. Every SAR image (noisy and ground truth) was split into 1600 images of 512 × 512 pixels, so a total of 3200 images were obtained. The dataset was also split into 3000 for training and 200 for validation, all of them available in four labeled folders.
ISSN: 2352-3409
DOI: 10.1016/j.dib.2024.110065
Source: Data in Brief[EISSN 2352-3409],v. 53, (Abril 2024)
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