Novel classification method for remote sensing images based on information entropy discretization algorithm and vector space model

Various kinds of remote sensing image classification algorithms have been developed to adapt to the rapid growth of remote sensing data. Conventional methods typically have restrictions in either classification accuracy or computational efficiency. Aiming to overcome the difficulties, a new solution for remote sensing image classification is presented in this study. A discretization algorithm based on information entropy is applied to extract features from the data set and a vector space model (VSM) method is employed as the feature representation algorithm. Because of the simple structure of the feature space, the training rate is accelerated. The performance of the proposed method is compared with two other algorithms: back propagation neural networks (BPNN) method and ant colony optimization (ACO) method. Experimental results confirm that the proposed method is superior to the other algorithms in terms of classification accuracy and computational efficiency. HighlightsA novel classification method is proposed for remote sensing image.The method is based on information entropy discretization algorithm, VSM, and KNN.The method outperforms the BPNN and ACO method on accuracy and efficiency.The accuracy increases with the feature dimensionality or training set size growth.

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