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arXiv · 2001.04716

Edge Preserving CNN SAR Despeckling Algorithm

Sergio Vitale·Giampaolo Ferraioli·Vito Pascazio
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Abstract

SAR despeckling is a key tool for Earth Observation. Interpretation of SAR images are impaired by speckle, a multiplicative noise related to interference of backscattering from the illuminated scene towards the sensor. Reducing the noise is a crucial task for the understanding of the scene. Based on the results of our previous solution KL-DNN, in this work we define a new cost function for training a convolutional neural network for despeckling. The aim is to control the edge preservation and to better filter manmade structures and urban areas that are very challenging for KL-DNN. The results show a very good improvement on the not homogeneous areas keeping the good results in the homogeneous ones. Result on both simulated and real data are shown in the paper.

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Sergio Vitale, Giampaolo Ferraioli, Vito Pascazio. 2020-01-14. Edge Preserving CNN SAR Despeckling Algorithm. https://doi.org/10.1109/lagirs48042.2020.9165559

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