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Research Article | Open Access |

Ground Control Point Generation from Simulated SAR Image Derived from Digital Terrain Model and its Application to Texture Feature Extraction

Author 1: Kohei Arai
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 12, No. 1 · Published 2021

DOI: https://doi.org/10.14569/IJACSA.2021.0120112

Abstract

Ground Control Point: GCP generation from simulated topographic map derived from Digital Terrain Model: DTM is proposed. Also, texture feature extraction is attempted from the simulated image. In this study, simulated image is derived from elevation data only, under assumptions of a simple scattering model without consideration of complex dielectric constant of the targets of interest. The performance of the acquired GCPs was evaluated by using several measures with texture features of GCP chip images. This paper describes the details about proposed method for acquisition of GCPs and simulated results on relationship between texture features and GCP matching success rate corresponding to the cross correlation between reference and distorted GCP chip images.

Keywords

How to Cite this Article

Arai, K. (2021). Ground Control Point Generation from Simulated SAR Image Derived from Digital Terrain Model and its Application to Texture Feature Extraction. International Journal of Advanced Computer Science and Applications, 12(1). https://doi.org/10.14569/IJACSA.2021.0120112

Arai, Kohei. "Ground Control Point Generation from Simulated SAR Image Derived from Digital Terrain Model and its Application to Texture Feature Extraction." International Journal of Advanced Computer Science and Applications, vol. 12, no. 1, 2021, https://doi.org/10.14569/IJACSA.2021.0120112.

@article{Arai2021,
  title     = {Ground Control Point Generation from Simulated SAR Image Derived from Digital Terrain Model and its Application to Texture Feature Extraction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {1},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJACSA.2021.0120112},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120112}
}

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