Image Fusion Based on Extensions of Independent Component Analysis

Remote sensing image fusion can effectively improve the accuracy of image classification. This paper proposes an image fusion algorithm based on extensions of independent component analysis (ICA) and multi-classifier system. Firstly a novel method of fusing panchromatic and multi-spectral remote sensing images is developed by contourlet transform which can offer a much richer set of directions and shapes than wavelet. As ICA not only can effectively remove the correlation of multi-spectral images, but also can realize sparse coding of images and capture the essential edge structures and textures of images, then using features extracted from the extension of ICA domain coefficients of the fused image, different classifiers corresponding to different image features are chosen in parallel style and the support vector machines are trained to classify the whole fused image as stack part in the proposed multi-classifier system. Experimental results show that the proposed algorithm can effectively improve the accuracy of image classification. * Corresponding author.