A Study of A Proposed Suboptimal Selection Strategy Based On Genetic Algorithm And Filters of Mutual Information
How to Cite?
Merzouqi Maria, Agouzal Mehdi, Sarhrouni El kebir, Hammouch Ahmed, "A Study of A Proposed Suboptimal Selection Strategy Based On Genetic Algorithm And Filters of Mutual Information," International Journal of Engineering Trends and Technology, vol. 69, no. 11, pp. 71-82, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I11P209
A novel approach to reduce the dimensionality of hyperspectral images which adopts the selection strategy has been proposed in this work. This approach introduces new genetic algorithm hybridization with one of the mutual information filters (CIM, MRLR, MRMR, NMIFS, MIFS_U2, MIFSU, and MIFS), which have been integrated as its fitness function. Also, this work makes a comparative study of the behavior of genetic algorithms with these filters in the selection of bands. The validation of the result is calculated by two classifiers (RBF-SVM and KNN) to judge the behavior of the hybridization introduced using the overall accuracy (OA %). The new approach is performed on hyperspectral datasets (Pavia, Indian Pines, and Salinas). The results obtained reveal that the performance of this hybridization in selecting the minimum of bands (ex: 30 bands of Salinas) exceeds 95% of OA. According to the results, this approach proves that it can select the most relevant bands and control the redundancy positively. In terms of classification accuracy, they have fascinating results in an acceptable processing time. The results confirm that this hybridization can discriminate between the relevant and irrelevant redundancy bands, efficiently reducing the HSI. Especially the first proposal CMI_GA, except that other comparison hybridization could also fight against the large dimension of these images, precisely those which are not controlled by the adjustable parameter (MRLR_GA with OA =93.93% for only 25 bands of Pavia, and OA=93.44 % for 30 bands of Indiana Pine).
Reduction of dimensionality, mutual information filters, genetic algorithm, classification, RBF-SVM, hybridization strategy.
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