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Research Article | Open Access |

Method for Image Quality Evaluation of Satellite-based SAR Data

Author 1: Kohei Arai Author 2: Michihiro Mikamo Author 3: Shunsuke Onishi
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 14, No. 7 · Published 2023

DOI: https://doi.org/10.14569/IJACSA.2023.0140713

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Abstract

A method for image quality evaluation of satellite-based Synthetic Aperture Radar: SAR data is proposed. Not only geometric fidelity but also signal to noise ratio, frequency component, saturated pixel ratio, speckle noise, optimum filter kernel size and its filter function are evaluated. Through experiments with SAR so called QPS-SAR_2 (Q-shu Pioneers of Space SAR the second) of imagery data, all these items are evaluated, and it is confirmed that the geometric and radiometric performances are good enough. Also, geometric fidelity of QPS-SAR_2 is compared to Sentinel-1/SAR European Space Agency (ESA) provided data which is obtained on the same day of QPS-SAR_2 data acquisition.

Keywords

Image quality synthetic aperture radar (SAR) geometric fidelity signal to noise ratio frequency component saturated pixel ratio speckle noise optimum filter kernel size filter function for speckle noise reduction

How to Cite this Article

Arai, K., Mikamo, M., & Onishi, S. (2023). Method for Image Quality Evaluation of Satellite-based SAR Data. International Journal of Advanced Computer Science and Applications, 14(7). https://doi.org/10.14569/IJACSA.2023.0140713

Arai, Kohei, et al.. "Method for Image Quality Evaluation of Satellite-based SAR Data." International Journal of Advanced Computer Science and Applications, vol. 14, no. 7, 2023, https://doi.org/10.14569/IJACSA.2023.0140713.

@article{Arai2023,
  title     = {Method for Image Quality Evaluation of Satellite-based SAR Data},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {7},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Michihiro Mikamo and Shunsuke Onishi},
  doi       = {10.14569/IJACSA.2023.0140713},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140713}
}

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