A Novel Cost Function for Despeckling using Convolutional Neural Networks

被引：11

作者：

Ferraioli, Giampaolo ^{[1
]}

Pascazio, Vito ^{[2
]}

Vitale, Sergio ^{[2
]}

机构：

[1] Univ Napoli Parthenope, Dipartimento Sci & Tecnol, Naples, Italy

[2] Univ Napoli Parthenope, Dipartimento Ingn, Naples, Italy

来源：

2019 JOINT URBAN REMOTE SENSING EVENT (JURSE) | 2019年

关键词：

SAR; speckle; cnn; despeckling; deep learning; NOISE;

D O I：

10.1109/jurse.2019.8809042

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Removing speckle noise from SAR images is still an open issue. It is well know that the interpretation of SAR images is very challenging and despeckling algorithms are necessary to improve the ability of extracting information. An urban environment makes this task more heavy due to different structures and to different objects scale. Following the recent spread of deep learning methods related to several remote sensing applications, in this work a convolutional neural networks based algorithm for despeckling is proposed. The network is trained on simulated SAR data. The paper is mainly focused on the implementation of a cost function that takes account of both spatial consistency of image and statistical properties of noise.

引用

页数：4

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