Category Decomposition-based Within Pixel Information Retrieval Method and its Application to Partial Cloud Extraction from Satellite Imagery Pixels

Category decomposition-based within pixel information retrieval method is proposed together with its application to partial cloud extraction from satellite imagery pixels. A comparative study was conducted for estimation of the sea surface temperature of the pixel suffered from partial cloud cover within a pixel. Three methods for estimation of partial cloud cover within a pixel, based on the proposed category decomposition-based method with Generalized Inverse Matrix Method: GIMM and well-known Least Square Method: LSM and Maximum Likelihood Method: MLH, were compared. It was found that around 9% of RMS (Root Mean Square) error can be achieved. Also, it was found that estimation accuracy highly depends on variance of representative vectors for cloud and the ocean or observed noise. The experimental results with simulated data show RMS error of GIMM are highly dependent to the noise followed by MLH and LSM. The results also show the best estimation accuracy can be achieved for MLH followed by LSM and GIMM.

@article{Arai2024,
  title     = {Category Decomposition-based Within Pixel Information Retrieval Method and its Application to Partial Cloud Extraction from Satellite Imagery Pixels},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Yasunori Terayama and Masao Moriyama},
  doi       = {10.14569/IJACSA.2024.0150129},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150129}
}