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RETRACTED ARTICLE: Image denoising and despeckling methods for SAR images to improve image enhancement performance: a survey

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Abstract

The synthetic aperture radar (SAR) images are playing an essential role in remote sensing. Various types of internal, external, and environmental noise are affecting the SAR images. Coherent speckle noise is the primary source of noise in SAR images. Such noise can be removed by using a single filter or combination of filters and transform signals. SAR image denoising has been attracting the attention of researchers for the past three decades. The target area and application type are influencing the choice of denoising method. In this paper, the basics of SAR imaging, steps in the pipeline of SAR despeckling process, filters like Lee filter, Frost filter, Kuan Filter and Gamma Maximum a posteriori (MAP) filter, various state of the art despeckling methods and deep learning approaches for SAR despeckling are discussed. Five transforms for despeckling are discussed with literature. The data sets from different radars, the applications, area of importance, and the quality metrics used to evaluate the despeckling quality are discussed in detail that has been available in the literature of the past two decades.

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    Ponmani E. &  Saravanan P.

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Ponmani E., Saravanan P. RETRACTED ARTICLE: Image denoising and despeckling methods for SAR images to improve image enhancement performance: a survey. Multimed Tools Appl 80, 26547–26569 (2021). https://doi.org/10.1007/s11042-021-10871-7

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