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Research Article | Open Access |

Polarimetric SAR Image Classification with High Frequency Component Derived from Wavelet Multi Resolution Analysis: MRA

Author 1: Kohei Arai
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 2, No. 9 · Published 2011 · Cited by 17

DOI: https://doi.org/10.14569/IJACSA.2011.020907

Abstract

A method for polarimetric Synthetic Aperture Radar: SAR image classification with high frequency component derived from wavelet Multi-Resolution Analysis: MRA is proposed. Although it is well known that polarization signature derived from fully polarized SAR data is useful for SAR image classifications, it is still unknown how to utilize the polarization signature in the image classification. High frequency component of the polarization signature calculated with the fully polarized SAR data is taken as one of the features utilizing in the classification into account. Thus improvement of classification performance is achieved for the proposed classification method of which such feature is included in the feature space for the Maximum Likelihood based classification method.

Keywords

How to Cite this Article

Arai, K. (2011). Polarimetric SAR Image Classification with High Frequency Component Derived from Wavelet Multi Resolution Analysis: MRA. International Journal of Advanced Computer Science and Applications, 2(9). https://doi.org/10.14569/IJACSA.2011.020907

Arai, Kohei. "Polarimetric SAR Image Classification with High Frequency Component Derived from Wavelet Multi Resolution Analysis: MRA." International Journal of Advanced Computer Science and Applications, vol. 2, no. 9, 2011, https://doi.org/10.14569/IJACSA.2011.020907.

@article{Arai2011,
  title     = {Polarimetric SAR Image Classification with High Frequency Component Derived from Wavelet Multi Resolution Analysis: MRA},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {2},
  number    = {9},
  year      = {2011},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJACSA.2011.020907},
  url       = {https://doi.org/10.14569/IJACSA.2011.020907}
}

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