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Automated Speckle Noise Suppression from Sentinel-1A Synthetic Aperture Radar Imagery Using Adaptive Filtering Techniques

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Inventive Communication and Computational Technologies

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 383))

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Abstract

Synthetic aperture radar (SAR) imagery is formed by measuring the backscattered microwave energy to the satellite. However, it is well known that such recorded SAR data is amended by inevitable multiplicative noise known as speckle due to the interference of the returning electromagnetic waves scattered from multiple surfaces. This results in granular appearances in SAR imagery, which not only exacerbate the overall quality (edge and texture information) and exert hindrance to the understanding of the image features but also make target detection challenging. In order to effectively analyze SAR imagery, speckle (the high frequency) must often be suppressed to the greatest extent possible before the image can be used for further analysis. In this paper, despeckling is performed by applying MAP, Lee, Frost, Kuan and Boxcar adaptive filtering techniques with 3 × 3, 5 × 5 and 7 × 7 sized kernels. Evaluation of these adaptive filtering techniques is then performed using metrics—speckle suppression index, enhanced number of looks, noise variance, mean square error, mean and standard deviation. It is observed from the experimental results that the Lee filter with 5 × 5 sized kernel has produced reasonably good results with high enhanced number of looks value, low mean square error, speckle suppression index, noise variance values, and a higher percentage change in standard deviation compared to that from the MAP, Frost, Kuan and Boxcar filters. In addition to these quantitative measures, qualitative visual inspection of the despeckled image is also performed to compare results from these filters.

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Authors and Affiliations

  1. Computer Science and Engineering, Vidya Jyothi Institute of Technology, Hyderabad, India

    N. Anusha

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  1. N. Anusha
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Correspondence to N. Anusha .

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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, Tamil Nadu, India

    G. Ranganathan

  2. Ryerson Communications Lab Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Xavier Fernando

  3. ISEG, University of Lisbon, Lisboa, Portugal

    Álvaro Rocha

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Anusha, N. (2023). Automated Speckle Noise Suppression from Sentinel-1A Synthetic Aperture Radar Imagery Using Adaptive Filtering Techniques. In: Ranganathan, G., Fernando, X., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 383. Springer, Singapore. https://doi.org/10.1007/978-981-19-4960-9_59

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