A systematic review on recent developments in nonlocal and variational methods for SAR image despeckling

Baraha, Satyakam; Sahoo, Ajit Kumar; Modalavalasa, Sowjanya

doi:10.1016/j.sigpro.2022.108521

Cited by 24 publications

(13 citation statements)

References 122 publications

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“…Among the approaches used for SAR images despeckling, Bayesian methods [60][61][62][63][64], non-Bayesian algorithms [65][66][67], hybrid approaches, and also new methodologies based on machine learning algorithms can be mentioned. Comprehensive recent reviews of these methodologies can be found in [68][69][70].…”

Section: Denoisingmentioning

confidence: 99%

Monitoring Braided River-Bed Dynamics at the Sub-Event Time Scale Using Time Series of Sentinel-1 SAR Imagery

et al. 2023

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Remote sensing plays a central role in the assessment of environmental phenomena and has increasingly become a powerful tool for monitoring shorelines, river morphology, flood-wave delineation and flood assessment. Optical-based monitoring and the characterization of river evolution at long time scales is a key tool in fluvial geomorphology. However, the evolution occurring during extreme events is crucial for the understanding of the river dynamics under severe flow conditions and requires the processing of data from active sensors to overcome cloud obstructions. This work proposes a cloud-based unsupervised algorithm for the intra-event monitoring of river dynamics during extreme flow conditions based on the time series of Sentinel-1 SAR data. The method allows the extraction of multi-temporal series of spatially explicit geometric parameters at high temporal and spatial resolutions, linking them to the hydrometric levels acquired by reference gauge stations. The intra-event reconstruction of inundation dynamics has led to (1) the estimation of the relationship between hydrometric level and wet area extension and (2) the assessment of bank erosion phenomena. In the first case, the behavior exhibits a change when the hydrometric level exceeds 1 m. In the second case, the erosion rate and cumulative lateral erosion were evaluated. The maximum erosion velocity was greater than 1 m/h, while the cumulative lateral erosion reached 130 m. Time series of SAR acquisitions, provided by Sentinel-1 satellites, were analyzed to quantify changes in the wet area of a reach of the Tagliamento river under different flow conditions. The algorithm, developed within the Python-API of GEE, can support many types of analyses of river dynamics, including morphological changes, floods monitoring, and bio-physical habitat dynamics. The results encourage future advancements and applications of the algorithm, specifically exploring SAR data from ICEYE and Capella Space constellations, which offer significantly higher spatial and temporal resolutions compared to Sentinel-1 data.

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Section: Denoisingmentioning

confidence: 99%

Monitoring Braided River-Bed Dynamics at the Sub-Event Time Scale Using Time Series of Sentinel-1 SAR Imagery

et al. 2023

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“…For the MuLoG + BM3D algorithm, the free parameters are set as suggested in the reference paper [53]. These images have the range [1,255] For a fair comparison, we present the PSNR, MAE, SSIM, and CPU Times values of the restoration results in Tables 2 and 3. These tables show that the proposed model solved by the SAV algorithm achieves higher PSNR and SSIM values.…”

Section: Numerical Implementationsmentioning

confidence: 99%

“…Digital images are often affected by various external physical conditions during the processes of storage, transmission, and transformation, resulting in quality degradation, which not only affects the visualization of images, but also causes difficulties in the subsequent processing and application of the image. Therefore, image denoising has always been a hot research topic in image processing [1]. Noise in images can be roughly divided into two categories: additive noise and multiplicative noise.…”

Section: Introductionmentioning

confidence: 99%

Multiplicative Noise Removal and Contrast Enhancement for SAR Images Based on a Total Fractional-Order Variation Model

Zhou

Guo

et al. 2023

Fractal Fract

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In this paper, we propose a total fractional-order variation model for multiplicative noise removal and contrast enhancement of real SAR images. Inspired by the high dynamic intensity range of SAR images, the full content of the SAR images is preserved by normalizing the original data in this model. Then, we propose a degradation model based on the nonlinear transformation to adjust the intensity of image pixel values. With MAP estimator, a corresponding fidelity term is introduced into the model, which is beneficial for contrast enhancement and bias correction in the denoising process. For the regularization term, a gray level indicator is used as a weighted matrix to make the model adaptive. We first apply the scalar auxiliary variable algorithm to solve the proposed model and prove the convergence of the algorithm. By virtue of the discrete Fourier transform (DFT), the model is solved by an iterative scheme in the frequency domain. Experimental results show that the proposed model can enhance the contrast of natural and SAR images while removing multiplicative noise.

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“…Its capacity to draw clear information even from messy real-world data improving the precision and reliability of measurements has made it a key tool for boosting real-time decision-making abilities [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87]. The fusion of polarimetric sensing and Kalman filtering techniques is an amalgam of beauty theory and practical performance, which opens up the potential of what could be achieved with contemporary technology [88][89][90][91][92][93][94][95][96][97][98][99].…”

Section: Introductionmentioning

confidence: 99%

The rise of the polarimetric Kalman filter: a bibliometric study on its growing significance

Obaideen,

AlShabi,

Gadsden

et al. 2024

Polarization: Measurement, Analysis, and Remote Sensing XVI

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This paper presents a comprehensive study on the evolution, applications, and impact of the Polarimetric Kalman Filter (PKF) in the fields of signal processing and remote sensing. By employing a methodological framework that integrates literature collection, screening and selection, bibliometric analysis, and synthesis, we aim to elucidate the PKF's contributions and potential research trajectories within its applications. Our findings highlight the PKF's significant role in enhancing data assimilation, improving predictive accuracy, and refining measurement techniques across various domains, particularly in meteorological research and environmental monitoring. The analysis reveals the interdisciplinary nature of PKF applications and its capability to merge theoretical advancements with practical implementations, underlining the importance of sophisticated signal processing algorithms in interpreting complex environmental data. The study also identifies emerging trends, such as the integration of PKF with machine learning and artificial intelligence, indicating future directions for research that promise to push the boundaries of current methodologies and applications. This paper underscores the PKF's pivotal role in advancing signal processing and remote sensing technologies, offering insights into its continuing development and the expanding scope of its applications.

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A systematic review on recent developments in nonlocal and variational methods for SAR image despeckling

Cited by 24 publications

References 122 publications

Monitoring Braided River-Bed Dynamics at the Sub-Event Time Scale Using Time Series of Sentinel-1 SAR Imagery

Monitoring Braided River-Bed Dynamics at the Sub-Event Time Scale Using Time Series of Sentinel-1 SAR Imagery

Multiplicative Noise Removal and Contrast Enhancement for SAR Images Based on a Total Fractional-Order Variation Model

The rise of the polarimetric Kalman filter: a bibliometric study on its growing significance

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