Edge Preserving CNN Sar Despeckling Algorithm

Vitale, Sergio; Ferraioli, Giampaolo; Pascazio, Vito

doi:10.5194/isprs-archives-xlii-3-w12-2020-97-2020

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…The design of the proposed network architecture comes from the results achieved in our previous works [34], [39], where ten layers of CNNs with different cost functions were proposed.…”

Section: Network Architecturementioning

confidence: 99%

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Multi-Objective CNN-Based Algorithm for SAR Despeckling

Vitale

Ferraioli

Pascazio

2021

IEEE Trans. Geosci. Remote Sensing

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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 the synthetic aperture radar (SAR) domain, the application of DL techniques is not straightforward due to the nontrivial interpretation of SAR images, especially caused by the presence of speckle. Several DL 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 article, 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 these terms is dedicated mainly to one of the following SAR image characteristics: spatial details, speckle statistical properties, and strong scatterers identification. Their combination allows balancing these effects. Moreover, a specifically designed architecture is proposed to effectively extract distinctive features within the considered framework. Experiments on simulated and real SAR images show the accuracy of the proposed method compared with the state-of-art despeckling algorithms, both from a quantitative and qualitative point of view. The importance of considering such SAR properties in the cost function is crucial for correct noise rejection and details preservation in different underlined scenarios, such as homogeneous, heterogeneous, and extremely heterogeneous.

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“…The design of the proposed network architecture comes from the results achieved in our previous works [34], [39], where ten layers of CNNs with different cost functions were proposed.…”

Section: Network Architecturementioning

confidence: 99%

“…Starting from the result of [39], the proposed neural network is composed of 17 convolutional layers. For each layer, we consider rectified linear unit (ReLU) as activation function [40], but for the last.…”

Section: Network Architecturementioning

confidence: 99%

Multi-Objective CNN-Based Algorithm for SAR Despeckling

Vitale

Ferraioli

Pascazio

2021

IEEE Trans. Geosci. Remote Sensing

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“…speckle suffer from artifacts due to the spatial correlation of speckle when applied to actual SAR images [42]. Combining a spatial loss with a spectral term [43] along with an edgepreserving term [44] still produces artifacts in homogeneous areas. The oversampling and spectral windowing operations are indeed not taken into account by Goodman's speckle model.…”

Section: B Deep Learning-based Techniquesmentioning

confidence: 99%

Image Restoration for Remote Sensing: Overview and Toolbox

Rasti¹,

Chen²,

Dalsasso³

et al. 2021

Preprint

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This paper is under review in GRSM. Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.

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Edge Preserving CNN Sar Despeckling Algorithm

Cited by 2 publications

References 17 publications

Multi-Objective CNN-Based Algorithm for SAR Despeckling

Multi-Objective CNN-Based Algorithm for SAR Despeckling

Image Restoration for Remote Sensing: Overview and Toolbox

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