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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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

Author 1: Kohei Arai

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0111206

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 12, 2020.

  • Abstract and Keywords
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Abstract: 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.

Keywords: Search engine; fuzzy expression; knowledge base system; membership function; mixed pixel: Mixel; context information; inverse problem solving

Kohei Arai, “Context Classification based on Mixing Ratio Estimation by Means of Inversion Theory” International Journal of Advanced Computer Science and Applications(IJACSA), 11(12), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111206

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


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