A Survey on Lee Filter and Its Improved Variants

Banerjee, Sriparna; Chaudhuri, Sheli Sinha; Mehra, Raghav; Misra, Arundhati

doi:10.1007/978-981-15-9433-5_36

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Among these interferences, noise remains a dominant factor [19], prompting the development of numerous methods for SAR noise suppression [20]. In engineering, filter-based techniques are commonly employed for noise suppression, with the Lee filter [21] and Kuan filter [22] being typical examples. These methods utilize the statistical distribution of the image area and adaptively estimate the reference pixel's actual value based on its neighboring pixels.…”

Section: Introductionmentioning

confidence: 99%

A TLRTV Dual-Band SAR Image Denoise–Fusion Strategy and its Preliminary Experimental Analysis in Multiband Airborne Radar System

Xing,

Cui,

Wang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Multi-band synthetic aperture radar (SAR) image fusion combines the scattering characteristics of targets from different bands to provide a comprehensive and informative output. However, the conventional SAR denoise and fusion separation framework often encounters the loss of fine details. This problem arises due to the inherent conflict between noise filtering and edge preservation. To address this issue, this paper proposes the joint tensor-based low-rank total variation (TLRTV) dual-band SAR image denoise-fusion strategy. The proposed strategy formulates the denoise-fusion problem by integrating the LR and TV models. Furthermore, this problem is extended to a high-dimensional form using tensor representation. To effectively solve the TLRTV problem, an optimization method is developed based on the alternating direction method of multipliers. This method decomposes the TLRTV problem into a series of subproblems, allowing for an efficient and accurate solution. To evaluate the performance of the proposed TLRTV method, the real measurement dual-band SAR images obtained from our developed airborne multi-band SAR system are utilized to compare with other existing denoise and fusion separation methods. The extensive experimental results demonstrate the superiority of the TLRTV approach achieving better fusion results, particularly in the presence of noise interference.

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Section: Introductionmentioning

confidence: 99%

A TLRTV Dual-Band SAR Image Denoise–Fusion Strategy and its Preliminary Experimental Analysis in Multiband Airborne Radar System

Xing,

Cui,

Wang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…Adaptive filters are better at preserving edges and details in high-texture areas (e.g. urban areas), and some examples are given by the Lee, the Frost, and the Refined Gamma Maximum-A-Posteriori (RGMAP) filters [26], [27], [28]. With respect to the non-adaptive filters, there are two forms of speckle filtering: one based on the mean and one based upon the median.…”

Section: Introductionmentioning

confidence: 99%

A Speckle Filter for Sentinel-1 SAR Ground Range Detected Data Based on Residual Convolutional Neural Networks

Sebastianelli

Rosso

Ullo

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In recent years, machine learning (ML) algorithms have become widespread in all the fields of remote sensing (RS) and earth observation (EO). This has allowed the rapid development of new procedures to solve problems affecting these sectors. In this context, this work aims at presenting a novel method for filtering speckle noise from Sentinel-1 ground range detected (GRD) data by applying deep learning (DL) algorithms, based on convolutional neural networks (CNNs). The paper provides an easy yet very effective approach to extract the large amount of training data needed for DL approaches in this challenging case. The experimental results on simulated speckled images and an actual SAR dataset show a clear improvement with respect to the state of the art in terms of peak signal-tonoise ratio (PSNR), structural similarity index (SSIM), equivalent number of looks (ENL), proving the effectiveness of the proposed architecture.

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“…Adaptive filters are better at preserving edges and details in high-texture areas (e.g. urban areas), and some examples are given by the Lee, the Frost, and the Refined Gamma Maximum-A-Posteriori (RGMAP) filters [24], [25], [26]. With respect to the non-adaptive filters, there are two forms of speckle filtering: one based on the mean and one based upon the median.…”

Section: Introductionmentioning

confidence: 99%

A speckle filter for SAR Sentinel-1 GRD data based on Residual Convolutional Neural Networks

Sebastianelli¹,

Rosso²,

Ullo³

et al. 2022

Preprint

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In recent years, Machine Learning (ML) algorithms have become widespread in all the fields of Remote Sensing (RS) and Earth Observation (EO). This has allowed the rapid development of new procedures to solve problems affecting these sectors. In this context, this work aims at presenting a novel method for filtering speckle noise from Sentinel-1 Ground Range Detected (GRD) data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs). The paper provides an easy yet very effective approach to extract the large amount of training data needed for DL approaches in this challenging case. The experimental results on simulated speckled images and an actual SAR dataset show a clear improvement with respect to the state of the art in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Equivalent Number of Looks (ENL), proving the effectiveness of the proposed architecture.

show abstract

A Survey on Lee Filter and Its Improved Variants

Cited by 6 publications

References 11 publications

A TLRTV Dual-Band SAR Image Denoise–Fusion Strategy and its Preliminary Experimental Analysis in Multiband Airborne Radar System

A TLRTV Dual-Band SAR Image Denoise–Fusion Strategy and its Preliminary Experimental Analysis in Multiband Airborne Radar System

A Speckle Filter for Sentinel-1 SAR Ground Range Detected Data Based on Residual Convolutional Neural Networks

A speckle filter for SAR Sentinel-1 GRD data based on Residual Convolutional Neural Networks

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