Abstract: Classification method with combined nonparametric and parametric classifications which depends on the normality of Probability Density Function of training samples is proposed. The proposed classification method is also based on spatial information for high spatial resolution of satellite based optical sensor images is proposed. Also, a classification method which takes into account not only spectral but also spatial features for LANDSAT-4 and 5 Thematic Mapper (TM) data is proposed. Treatment of the spatial-spectral variability existing within a region is more important for such high spatial resolution of satellite imagery data. Standard deviations in small cells, such as 2x2, 3x3 and 4x4 pixels, were used as measures to represent the spatial-spectral variabilities. This information can be used together with conventional spectral features in a unified way, for the traditional classifier such as the pixelwise Maximum Likelihood Decision Rule (MLHDR). The classification performance of new clear cuts and alpine meadows which are very close in spectral space characteristics and difficult to distinguish them by conventional methods are focused. Through experiments, it is found that there is a substantial improvement in overall classification accuracy for TM forestry data. The Probability of Correct Classification (PCC) for the new clear cuts and the alpine meadows classes rose by 7% to 97% correct. The confusion between alpine meadows and new clear cuts was reduced from 9% to 3%.

Kohei Arai, “Combined Non-parametric and Parametric Classification Method Depending on Normality of PDF of Training Samples” International Journal of Advanced Computer Science and Applications(IJACSA), 12(5), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120538

@article{Arai2021,
title = {Combined Non-parametric and Parametric Classification Method Depending on Normality of PDF of Training Samples},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120538},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120538},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {5},
author = {Kohei Arai}
}