PCENet: Deep SAR Despeckling Network Using Parallel Convolutional Encoding Modules

doi:10.1109/LGRS.2023.3344684

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PCENet: Deep SAR Despeckling Network Using Parallel Convolutional Encoding Modules

2024-01-01

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Anirban Saha *

K. R. Arihant

Suman Kumar Maji

DOI：10.1109/LGRS.2023.3344684

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Abstract

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Persistent phenomena of transmitted frequency interference (after reflecting off the target location) lead to the introduction of random speckle distributions in the raw data collected by synthetic aperture radar (SAR) sensors. The quality of the acquired images is thus degraded significantly due to the undesired speckle which creates a granular cover across the visual. Numerous techniques have been proposed in the literature which aim to remove this undesired speckle component. However, the objective of removing speckle while preserving minute structural and textural information captured by the raw data still remains an open problem. This letter proposes a unique SAR despeckling approach that uses parallel convolutional encoder (PCE) technique which captures highly effective feature components at various processing levels. In addition, the residual-based encoder module is structured in a way so that it can capture the interdependence among the parallelly extracted feature components. Optimal utilization of the proposed network structure enables efficient analysis and subsequent removal of the speckle components while retaining minute details captured by the raw data. Experimental results across both simulated and real SAR data strongly support the proposed model's superiority over various classical and state-of-the-art approaches described in the literature.

Keywords:

Speckle

Feature extraction

Visualization

Noise measurement

Mathematical models

Wiener filters

Convolution

Convolutional neural network (CNN)

encoder network

parallel convolutional encoder (PCE)

SAR denoising