A Study Of Information Fusion Applied To Remote Sensing And Identity Verification Systems

Information fusion deals with multisource data in order to get more new, significant information. Today, information fusion is becoming one of the most active researches, and is widely applied to all kinds of fields. The intention of the paper is to investigate the applications of information fusion in remote sensing and multi-modal biometric identity verification system.Different remote sensing images have different information, and the combination of multisensor images (fusion) can get more complete and reliable information. One of the keystones of the paper is image fusion algorithm based on pixel level and feature level.In the pixel-based fusion algorithm, we focus on image fusion based on wavelet transform. After the introduction of the wavelet theory and the principle of wavelet-based image fusion, we test the Radarsat and Landsat images using gray-value algorithm. When it comes to feature-based image fusion, we make use of Mean-Shift to extract appropriate features according to the characteristics of Radarsat and Landsat images, then apply the Bayes theory to feature level fusion classification.In recent years, biometric identity verification has a rapid development, multi-modal identity verification has gained more and more attention, and the combination of multimodal can improve the performance of the identity verification system. The other focus is multi-modal identity verification systems.Based on analysis of the recent research on the multi-modal fusion system, we investigate the parameter and non-parameter fusion systems. In the parameter-basedfusion methods, we analyze the applicability of the Bayes theory and Neyman-Pearson rule when they are applied to identity verification systems; we compare global and local parameter in Bayes fusion system; we further propose weighted method, and apply it to Bayes- and Neyman-Pearson- based identity verification system, and get a more higher verification result.In the non-parameters fusion methods, we use K-NN and ENN classifiers to combine different biometrics, and we propose improved ENN method. Compared with K-NN and ENN identity verification systems, the performance of verification system using improved ENN is enhanced.