A comprehensive review of SAR image filtering techniques: systematic survey and future directions

Painam, Ranjith Kumar; Manikandan, S.

doi:10.1007/s12517-020-06416-1

Cited by 9 publications

(4 citation statements)

References 41 publications

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“…Numerous methods were proposed for speckle reduction or despeckling. Speckle reduction filters are classified as non-adaptive and adaptive filters [ 23 ]. Mean and median filtering methods are examples of non-adaptive techniques.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, speckling reduction techniques are categorized as the spatial domain, transform domain (or wavelet domain), non-local filtering, and total variational [ 23 ]. Specifically, anisotropic diffusion, bilateral filter (BF), fast non-local means filter (FNLMF), and guided filter (GF) are some other filters to reduce speckle noise [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

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Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan

Kılıç

Nielsen

2022

Sensors

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In this study, principal component analysis and k-means clustering (PCAKM) methods for synthetic aperture radar (SAR) data are analyzed to reduce the sensitivity caused by changes in the parameters and input images of the algorithm, increase the accuracy, and make an improvement in the computation time, which are advantageous for scoring in the follow-up plan. Although there are many supervised methods described in the literature, unsupervised methods may be more appropriate in terms of computing time, data scarcity, and explainability in order to supply a trustworthy system. We consider the PCAKM algorithm, which is used as a benchmark method in many studies when making comparisons. Error metrics, computing times, and utility functions are calculated for 22 modified PCAKM regarding difference images and filtering methods. Various images with different characteristics affect the results of the configurations. However, it is evident that the PCAKM becomes less sensitive and more accurate for both the overall results and image results. Scoring by utilizing these results and other map information is a gap and innovation. Obtaining a change map in a fast, explainable, more robust and less sensitive way is one of the aims of our studies on scoring points in the follow-up plan.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan

Kılıç

Nielsen

2022

Sensors

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“…, where D m is the variance matrix of multiplicative errors, E aei is a diagonal matrix where the kth diagonal element is a k e i , and e i is the t-dimensional natural basis vector. According to equation ( 5), for the linear form of f(χ ), essentially D aχ can be understood as D aχ = diag(a i χ ) = diag(f(χ )), where the variance array of multiplicative errors Iσ 2 m is essentially the variance array of f(χ ). Therefore, when f(χ ) is in nonlinear form, its weight array P n represents:…”

Section: Ls Solutions For Mam Modelsmentioning

confidence: 99%

“…As modern observation techniques grows by leaps and bounds, the observations obtained by modern precision measurement techniques often have multiplicative errors associated with the observed signals or mixed additive and multiplicative (MAM) errors where additive and multiplicative errors coexist [1][2][3]. Therefore, the traditional adjustment processing methods based on additive errors can no longer meet high accuracy requirements of modern data processing.…”

Section: Introductionmentioning

confidence: 99%

Improved artificial gorilla troops optimizer with chaotic adaptive parameters - application to the parameter estimation problem of mixed additive and multiplicative random error models

Wang,

Han,

Pang

2023

Meas. Sci. Technol.

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For mixed additive and multiplicative random error models (MAM models), due to the complex correlation between the parameters and the model power array, derivative operations will be inevitable in the actual calculation. When the observation equation is in nonlinear form, the operations will be more complicated. The swarm intelligence optimization algorithm (SIO) can effectively solve the derivative problem when estimating the nonlinear model parameters using conventional iterative algorithms. However, for different problems, the conventional SIO cannot effectively balance the ability of global and local behavior, resulting in the algorithm falling into prematureness and failing to output effective parameter information. To address the above problems, the improved artificial gorilla troops optimizer (CAGTO) algorithm with chaotic adaptive behavior is proposed. To address the problem that the population generated by the algorithm using pseudo-random numbers in the initialization population phase has poor traversability in the feasible domain, the chaotic sequence is applied to initialize the population instead of pseudo-random number generation to ensure that the population can traverse the feasible domain as much as possible and improve the global search capability of the algorithm. Adaptive parameters that vary linearly and nonlinearly with the algorithm process are constructed to balance the global search and local search ability, while accelerating the convergence speed. Two CAGTO algorithms with different parameter settings are constructed for different problems, and the experimental results show that both CAGTO algorithms can effectively solve the parameter estimation problem of MAM models with different nonlinear forms of observation equations compared with several other comparative algorithms.

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Advances in Earth observation and machine learning for quantifying blue carbon

Pham

Saintilan

et al. 2023

Earth-Science Reviews

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A comprehensive review of SAR image filtering techniques: systematic survey and future directions

Cited by 9 publications

References 41 publications

Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan

Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan

Improved artificial gorilla troops optimizer with chaotic adaptive parameters - application to the parameter estimation problem of mixed additive and multiplicative random error models

Advances in Earth observation and machine learning for quantifying blue carbon

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