Speckle Reduction in SAR Images using CNN

Santhi, V.; Mohandass, Dheshan; Jayanthi, J.; Arulmozhivarman, P.; Mehra, Raghav

doi:10.1109/icspc51351.2021.9451809

Cited by 2 publications

(1 citation statement)

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“…Recently, various speckle filtering algorithms have been proposed [1][2][3][4] and used in pre-processing to limit the impacts of speckle in these applications. Many such algorithms in the literature [5,6] include Gaussian filtering, spatialpixel feature denoising algorithm, and bilateral filtering used to handle various fundamental noiseremoval tasks [7,8]. Reducing speckle noise without losing some fine features of the SAR image is challenging.…”

Section: Introductionmentioning

confidence: 99%

Resolving Interferometric SAR Speckle Problem Using Adaptive GAN

2022

IJIES

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The interference of reflected waves from multiple elementary scatterers produces speckle, which appears as a granular noise in synthetic aperture radar (SAR) images. These speckles in SAR images cause difficulty in image interpretation, which reduces the effectiveness of image segmentation and classification. In this paper, we propose an effective solution using generative adversarial networks (GAN) to decrease speckle noise while preserving texture features.The convolutional block attention module (CBAM) boosts the essential features in upsampled data and supports the creation of a final denoised real InSAR data.The proposed deep-learning method proved that CBAM enhanced the peak signal-to-noise ratio using the GAN. Furthermore, CBAM improved structural similarity (SSIM) to 99 and achieved the minimum mean squared error. The despeckle performance was enhanced using GAN-ResUNet in which the SSIM equals 0.999. The denoising performance proved that the use of GAN with CBAM as the generator inspired by ResUNeT achieved the best performance compared to other experiments results.

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