Maximum Likelihood Classification based on Classified Result of Boundary Mixed Pixels for High Spatial Resolution of Satellite Images

Maximum Likelihood Classification: MLC based on classified result of boundary Mixed Pixels (Mixel) for high spatial resolution of remote sensing satellite images is proposed and evaluated with Landsat Thematic Mapper: TM images. Optimum threshold indicates different results for TM and Multi Spectral Scanner: MSS data. This may since the TM spatial resolution is 2.7 times finer than MSS, and consequently, TM imagery has more spectral variability for a class. The increase of the spectral heterogeneity in a class and the higher number of channels being used in the classification process may play significant role. For example, the optimum threshold for classifying an agricultural scene using MSS data is about 2.5 standard deviations, while that for TM corresponds to more than four standard deviations. This paper compares the optimum threshold between MSS and TM and suggests a method of using unassigned boundary pixels to determine the optimum threshold. Further, it describes the relationship of the optimum threshold to the class variance with a full illustration of TM data. The experimental conclusions suggest to the user some systematic methods for obtaining an optimal classification with MLC.

@article{Arai2020,
  title     = {Maximum Likelihood Classification based on Classified Result of Boundary Mixed Pixels for High Spatial Resolution of Satellite Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {9},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJACSA.2020.0110904},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110904}
}