Satellite Multi-source Remote Sensing Image Fusion Technology Research

In recent years, multisensor image fusion techniques have attracted extensive attention in remote sensing application and image project area. Multisensor remotely sensed image fusion technique can combine multisensor images and produce a more precise, integrated and reliable estimation and description of them than a single image. According to the level in which the fusion implements, image fusion is divided into pixel-level fusion, feature-level fusion and decision-level fusion. The research in this paper is executed mainly at pixie level and feature level image fusion.At pixel level, on the basis of studying the elementary theories of multisensor data fusion and procedures of pretreatment in remote sensing image fusion, this dissertation summarizes the common methods which are applied in multisensor remotely sensed image fusion. Afterwards, in order to decrease the contradiction between the more complex and mass remote sensing image data and relatively slow speed of information extraction, an improved SFIM image fusion method is proposed. This modified algorithm is on the base of SFIM fusion technique, combines IHS method and SFIM method and then replaces the former mean filter by an adaptive weighted mean filter. Compared with the results of several common fusion techniques through a set of simulation tests between multispectral images and panchromatic images, it is proved that the new method can get an excellent result for the aim of improving spatial resolution while preserving the spectral information of multispectral images. Due to its simpleness, the proposed approach is more applicable for fast interactive processing and real time visualization.At feature level, this paper focuses on Markov random field-based image classification algorithms. Firstly, the cardinal theories of Markov random field, MAP-MRF based image classification algorithm and the combinatorial optimization methods during its implementation are elaborated. And then the paper describes EM-MRF iterative algorithm and its realization for the parameter estimation in unsupervised image classiifcation process. The EM-MRF-based image classification strategy is introduced into multisensorfeature-level image fusion, distributed and centric based fusion methods are proposed. Finally, simulated results through sythetic and real remotely sensed image illustrate the effectiveness and advantage of the proposed methods.