A SAR speckle filter based on Residual Convolutional Neural Networks

• URL: http://arxiv.org/abs/2104.09350v1
• Date: Mon, 19 Apr 2021 14:43:07 GMT
• Title: A SAR speckle filter based on Residual Convolutional Neural Networks
• Authors: Alessandro Sebastianelli, Maria Pia Del Rosso, Silvia Liberata Ullo
• Abstract summary: This work aims to present a novel method for filtering the speckle noise from Sentinel-1 data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs) The obtained results, if compared with the state of the art, show a clear improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM)
• Score: 68.8204255655161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, Machine Learning (ML) algorithms have become widespread in all fields of Remote Sensing (RS) and Earth Observation (EO). This has allowed a rapid development of new procedures to solve problems affecting these sectors. In this context, the authors of this work aim to present a novel method for filtering the speckle noise from Sentinel-1 data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs). The obtained results, if compared with the state of the art, show a clear improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index ({SSIM}), by proving the effectiveness of the proposed architecture. Moreover, the generated open-source code and dataset have been made available for further developments and investigation by interested researchers.

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