A novel QIM-DCT based fusion approach for classification of remote sensing images via PSO and SVM models

Abstract

Fusion of panchromatic and multispectral images has become a research interest for the classification of remote sensing images. The spectral and spatial resolutions of different images give better information with the aid of image classification. However, fusing pixels for various satellite images is difficult due to the nature of original image consists of complex information. Similarly, most of the existing fusion algorithms implement a unified processing over the whole part of the image, thereby leaving certain important needs out of consideration. The main aim of our proposed approach is to fuse the images by gathering all important information from multiple images with minimum errors. In this paper, we propose a novel quantization index modulation with discrete contourlet transform-based fusion approach for classification of remote sensing images (LISS IV sensor). In order to improve the image fusion performance, we eliminate certain noises (salt, pepper, and Gaussian) using Bayesian filter with Adaptive Type-2 Fuzzy System. After image fusion, we make image classification by two steps of processes including deep multi-feature extraction and feature selection. Multiple features such as spectral, shape, global and local features are extracted using Affine Transformation (0°, 90°, 180°, and 270°), and then the best set of features are chosen by mutual information and maximal information coefficients. Finally, the image is classified into seven classes using PSO and SVM namely Urban, Vegetation, Wetland, Tank, Water Area, Bare Land, and Roadways. MATLAB R2017b has been used for evaluation of the LISS IV images. Experimental results revealed that our proposed approach is very effective in terms of their classification accuracy.