The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-97-2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-97-2020
04 Nov 2020
 | 04 Nov 2020

EDGE PRESERVING CNN SAR DESPECKLING ALGORITHM

S. Vitale, G. Ferraioli, and V. Pascazio

Keywords: SAR, deep learning, speckle, cnn, denoising

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 man-made 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.