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MSPB: intelligent SAR despeckling using wavelet thresholding and bilateral filter for big visual radar data restoration and provisioning quality of experience in real-time remote sensing

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Abstract

The main reason behind degradation in Synthetic Aperture Radar (SAR) images is speckle noise which is a critical barrier of enhancing Quality of Experience (QoE) in remote sensing of environment. Speckle noise is multiplicative and behaves as a kind of granular pattern which is more an artifact such that a scattering phenomenon inherently exists in the SAR images. The SAR image despeckling is a technique to suppress the noise and preserve the edges (high-frequency information). This article presents a new Method noise wavelet thresholding-based SAR image despeckling using Pixel neighborhood and Bilateral filter (MSPB) for noise suppression and artifact reduction. In the proposed method, MSPB, wavelet-based thresholding is performed using an intelligent Bayesian thresholding rule followed by the method noise thresholding. The experimental outcomes of the MSPB are visually analyzed over the speckled SAR images. The despeckling results are compared to some conventional and some of the latest despeckling methods in the research topic. The despeckling process is also analyzed by image quality assessment (IQA) metrics including no-reference (e.g., ENL) and similarity-based objective (e.g., SNR) and subjective (e.g., SSIM) metrics to measure the quality of performance. The simulation results on some SAR image big datasets show that MSPB is efficient for offline and real-time applications.

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The conclusion and comparison data of this article are included within the article. For inquiries regarding raw data and codes, please contact the first author.

Abbreviations

DS:

Directional Smoothing

ENL:

Equivalent Number of Look

DWT:

Discrete Wavelet Transform

SAR:

Synthetic Aperture Radar

MSPB:

Method noise wavelet thresholding-based SAR image despeckling using Pixel neighborhood and Bilateral filter

NV:

Noise Variance

IQA:

Image Quality Assessment

PSNR:

Peak Signal-to-Noise Ratio

SSIM:

Structural Similarity

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Acknowledgements

We would like to sincerely thank the editors and reviewers for their time elapsed in handling and/or peer review.

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Authors and Affiliations

  1. Department of CSE, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India

    Prabhishek Singh & Achyut Shankar

  2. Department of CSE, Graphic Era (Deemed to Be University), Dehradun, Uttarakhand, India

    Manoj Diwakar

  3. Department of Computer Engineering, Persian Gulf University, Bushehr, Iran

    Mohammad R. Khosravi

Authors
  1. Prabhishek Singh
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  2. Achyut Shankar
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  3. Manoj Diwakar
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  4. Mohammad R. Khosravi
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Contributions

PS and AS have simulated the proposed technique, PS and MD have written the first draft, MRK and MD have coordinated scientific issues, and MRK has edited and finalized the manuscript for submission.

Corresponding author

Correspondence to Mohammad R. Khosravi.

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Singh, P., Shankar, A., Diwakar, M. et al. MSPB: intelligent SAR despeckling using wavelet thresholding and bilateral filter for big visual radar data restoration and provisioning quality of experience in real-time remote sensing. Environ Dev Sustain (2022). https://doi.org/10.1007/s10668-022-02395-3

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