Implementation and comparison of classifiers for different hyperspectral dataset based on machine learning algorithms
In these paper better classification accuracy techniques is proposed for flower and land cover hyperspectral dataset. Initially flower dataset is considered in which newly proposed improved particle swarm optimization is implemented and compared with particle swarm optimization and K means algorithm followed by land cover dataset is considered in which proposed random forest algorithm is compared with support vector machine and k means and Navie Bayes classifiers. In both the hyperspectral dataset proposed methods gives good classification results in terms of accuracy.
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