An efficient approach for data mining using PSO with differential evolution for satellite images

Abstract

Categorization of water bodies and land areas from the satellite image is performed since the prediction of satellite image has become a major challenging issue due to weather condition, atmosphere, etc. Previously, data mining is used for clustering in various application such as text data, similarities in images and bioinformatics data. In this paper, a novel approach has been designed by incorporating the PSO and DE algorithm for data mining technique in the satellite image. Here feature extraction is carried out by using DWT, PCA, and GLCM techniques. In the proposed method, an optimized PSO-DE algorithm is designed to obtain the best solution in order to get the better satellite data. Finally, the estimated output is compared with the existing method on the bases of performances, and it is found to be efficient. The performance parameters such as PSNR, MSE, RMS, mean, variance, correlation, contrast, energy, homogeneity, SD, and entropy are evaluated for the Landsat and MODIS satellite images.