Exploiting Aerial Imagery for Supervised Learning of SAR Despeckling Neural Networks
Lloyd Haydn Hughes, Shaunak De, Davide Castelletti, Ganesh Yalla · 2021
Many applications utilizing SAR data, such as change detection, segmentation and classification, are impaired by the multiplicative speckle interference inherent in the imagery. Thus despeckling of SAR imagery is a often the key to developing robust algorithms for scene understanding. In recent years numerous deep learning-based approaches to speckle reduction have been proposed. However, the performance of these methods has largely failed to meet the expectations of researchers and industry alike. A key reason for this is due to the lack of accurate SAR-based ground truth training data. In this paper we propose the use of very high-resolution (VHR), low speckle aerial imagery and an accurate speckle model, as a ground truth signal for training a despeckling network based on the DnCNN architecture. Furthermore, we investigate modifications to the training formulation and finally demostrate the approach on Capella-2 VHR X-band imagery.