IMPROVING INDEPENDENT COMPONENT ANALYSIS USING SUPPORT VECTOR MACHINES FOR MULTIMODAL IMAGE FUSION

Abstract

The objective of this study is to combine multiple images of a scene acquired by different sensors to create a new image with all important information from the i nput images. Recent studies show that bases trained using Independent Component Analysis (ICA) is effective in multisensor fusion and has improved performance over traditional wavelet approaches. In the ICA based fusion, the coefficients of the inpu t images are combined simply by selecting the coeffic ients with maximum magnitude. But this method resulted in fused images with poor contrast, due to the distortion introduced in constant background a reas. The performance of ICA based fusion can be greatly improved by using a region based approach with intelligent decision making in order to choose the significant regions in the source images. Hence, a new region based image fusion algorithm for combining visible and Infrared (IR) images using Independent Component Analysis and Support Vector Machines (SVM) is proposed. Region based joint segmentation of the source images is carried out in the spatial dom ain and important features of each region are compu ted in spatial and transform domain. A Support Vector Machine is trained to select the regions from the sourc e images with significant features and the correspond ing ICA coefficients are combined to form the fused ICA representation. The proposed algorithm is appli ed to different sets of multimodal images to valida te the robustness of the algorithm and compared with some standard image fusion methods. The fusion results demonstrate that the proposed scheme performs better than the state-of-the-art image fusion methods an d show a significant improvement in Entropy, Petrovic and Piella evaluation metrics.