Multi-Objective CNN Based Algorithm for SAR Despeckling

TL;DR

This paper introduces a novel CNN-based SAR despeckling algorithm that employs a multi-objective loss function to better preserve image details and statistical properties, outperforming existing methods on simulated and real data.

Contribution

It proposes a multi-objective loss function and a specialized CNN architecture tailored for SAR image despeckling, considering spatial and statistical properties.

Findings

Outperforms state-of-the-art despeckling algorithms in accuracy.

Effectively preserves spatial details and statistical properties.

Works well across different heterogeneity scenarios.

Abstract

Deep learning (DL) in remote sensing has nowadays become an effective operative tool: it is largely used in applications such as change detection, image restoration, segmentation, detection and classification. With reference to synthetic aperture radar (SAR) domain the application of DL techniques is not straightforward due to non trivial interpretation of SAR images, specially caused by the presence of speckle. Several deep learning solutions for SAR despeckling have been proposed in the last few years. Most of these solutions focus on the definition of different network architectures with similar cost functions not involving SAR image properties. In this paper, a convolutional neural network (CNN) with a multi-objective cost function taking care of spatial and statistical properties of the SAR image is proposed. This is achieved by the definition of a peculiar loss function obtained by the weighted combination of three different terms. Each of this term is dedicated mainly to one of the following SAR image characteristics: spatial details, speckle statistical properties and strong scatterers identification. Their combination allows to balance these effects. Moreover, a specifically designed architecture is proposed for effectively extract distinctive features within the considered framework. Experiments on simulated and real SAR images show the accuracy of the proposed method compared to the State-of-Art despeckling algorithms, both from quantitative and qualitative point of view. The importance of considering such SAR properties in the cost function is crucial for a correct noise rejection and details preservation in different underlined scenarios, such as homogeneous, heterogeneous and extremely heterogeneous.