A Review on SAR Image and its Despeckling

Singh, Prabhishek; Diwakar, Manoj; Shankar, Achyut; Shree, Raj; Kumar, Manoj

doi:10.1007/s11831-021-09548-z

Cited by 52 publications

(22 citation statements)

References 95 publications

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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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Section: Introductionmentioning

confidence: 99%

Resolving Interferometric SAR Speckle Problem Using Adaptive GAN

2022

IJIES

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“…The received SAR data in the form of high-resolution SAR images is resultant of consistent interaction of constructive and destructive interference of transmitted high-frequency radar waves with target on the Earth's surface. 5 This constructive and destructive interference causes a lot of information loss as well as quality degradation during the image acquisition phase. This information loss as well as quality degradation appears in the SAR image in the form of a scattering phenomenon.…”

Section: Introductionmentioning

confidence: 99%

“…This preprocessing is SAR image despeckling. 9 The elimination of speckle noise from SAR image is called as SAR image despeckling. This preprocessing method is a mandatory step in any kind of SAR image processing as it improves the image quality and makes further processing easier.…”

Section: Introductionmentioning

confidence: 99%

Review on nontraditional perspectives of synthetic aperture radar image despeckling

2022

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Synthetic aperture radar (SAR) image despeckling is a preprocessing method. SAR images are, by default, noisy in nature. The kind of noise found in SAR images is called speckle noise. The effect of this noise on SAR images is highly adverse. It degrades the quality of the SAR image, resulting in the loss of vital information. Since SAR images are inherently speckled in nature, it costs a lot of information loss. The removal of such noise from the SAR image is mandatory and is the first step. The elimination of speckle noise from SAR images is called SAR image despeckling. There are various traditional and nontraditional methods of SAR image despeckling based on Bayesian and non-Bayesian techniques. The SAR image despeckling methods based on Bayesian techniques are further subdivided into spatial and transform domains. This paper presents a comparative review of nontraditional perspectives on SAR image despeckling. The comparison is made based on methodology, objectives, merits, and demerits. Its focus is to do analysis of all the latest research done in the field of SAR image despeckling using nontraditional methods.

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“…Therefore, compared with the "point target" echo of narrowband radar, HRRP contains more abundant discriminative structural information. Compared with other broadband radar images, such as synthetic aperture radar (SAR) [2][3], HRRP has the advantage of low complexity in imaging and processing. These benefits make HRRP attract extensive attention and application in the field of radar automatic target recognition (RATR).…”

Section: Introductionmentioning

confidence: 99%

Template Construction of Radar Target Recognition based on Maximum Information Profile

Chen

Min

Liu

et al. 2022

J. Phys.: Conf. Ser.

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High resolution range profile (HRRP) has the advantages of low complexity in imaging and processing. These features make HRRP widely adopted in radar automatic target recognition technology. Template matching is a representative technique for radar target recognition using HRRP. To overcome the attitude sensitivity problem of HRRP data, it is necessary to subdivide the training database according to attitude domain, and construct corresponding templates for matching. However, templates from traditional methods usually cause the loss of details due to the smoothing process, with limited classification performance. This paper is motivated by proposing a template generation approach based on maximum information profile, which retains the detailed information in templates for accuracy optimization. The proposed method is verified by the recognition experiments of three types of aircraft targets. The experimental results indicate that the maximum information profile outperform the mean range profile contributed by the angle domain template.

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A Review on SAR Image and its Despeckling

Cited by 52 publications

References 95 publications

Resolving Interferometric SAR Speckle Problem Using Adaptive GAN

Resolving Interferometric SAR Speckle Problem Using Adaptive GAN

Review on nontraditional perspectives of synthetic aperture radar image despeckling

Template Construction of Radar Target Recognition based on Maximum Information Profile

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