부산대학교 도서관

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 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 letter, a methodology to design a realistic dataset is proposed. Actual SAR images of the same scene are acquired with the same sensor on different dates, and then they are properly coregistered 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 DL 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 an autoencoder (AE). The validation of 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.