Context Classification based on Mixing Ratio Estimation by Means of Inversion Theory

A contextual image classification method with a proportion estimation of the pixels composed of several classes, Mixed pixels (Mixels), is proposed. The method allows us to check the connectivity of separated road segments, which are observed frequently as discontinuity of roads in satellite remote sensing imagery. Under the assumption of almost same proportions for the Mixels in the discontinuous portion of road segments, a proportion estimation method utilizing Inverse Problem Solving is proposed. The experimental results with the simulation data including observation noise show 73.5~98.8(%) of improvements in terms of proportion estimation accuracy (Root Mean Square: RMS error), compared to the results from the previously proposed method with generalized inverse matrix. Also, usefulness of contextual classification based on the proposed proportion estimation was confirmed for the investigation of connectivity of roads in remotely sensed images from space.

@article{Arai2020,
  title     = {Context Classification based on Mixing Ratio Estimation by Means of Inversion Theory},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {12},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJACSA.2020.0111206},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111206}
}