A Novel Cost Function for Despeckling using Convolutional Neural Networks
This work addresses the challenge of improving information extraction from SAR images for remote sensing applications, but it appears incremental as it builds on existing deep learning methods with a new cost function.
The paper tackled the problem of removing speckle noise from SAR images, particularly in urban environments, by proposing a convolutional neural network trained on simulated data with a novel cost function that considers spatial consistency and noise statistics.
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.