Based On The Characteristics Of Multi-source Remote Sensing Image Fusion Research

Multi-source remote sensing image fusion is the same area or the image of thesame scene obtained by the different sensors are fused, the fused image higherreliability, fuzzy less understandable and better, more suitable for human visualComputer detection, classification, recognition, understanding and treatment.Multi-source remote sensing image fusion is more or at the level of the pixel level,However, as more and more demand for people interested in remote sensing imagetarget identification and tracking, pixel-level fusion method appears to more to theless able to meet this demand. Images based on feature level fusion is dependent onthe extraction of image area features a fusion level, because it is a premise based onimage features, so this combination method can effectively meet the image targetrecognition and tracking purposes.The general steps of the integration of remote sensing images, the imagepre-processing, registration, and then the image fusion. Registration in the imageaspects, the first were described in detail the similarity based on spatial relationshipsand feature-based registration method; Then, on the basis of these two registrationmethods make full use of the advantages of these two methods presents a The newcombination based on spatial relationships and characteristics similar to theregistration method. Registration experiments showed that the combination of spatialrelationships and characteristics similar registration method with quasi-image effectthan separately by the two methods better alignment, the shorter the time required forregistration.Image registration based on research in the third part of the two methods basedon feature level fusion methods: Kalman filtering and multi-feature combination ofthe noisy image fusion, multi-channel Gabor filtering for multi-feature fusion, and Based on this proposed image fusion algorithm based on quadratic integration ofmulti-feature. Kalman filtering and multi-feature combination of the noisy imagefusion method is through the source image after the Kalman filter, to mention toremove the image characteristics and image segmentation form, and then by formsimilarity fusion. Gabor filter for image fusion of multi-feature texture features forimage processing in order to achieve the final image for the effective integration of.Secondary fusion of multi-feature-based image fusion method is obtained by FCMmethod to split the regional characteristics of the matrix after principal componentanalysis approach to dimensionality reduction and then fused. Three fusion methods,followed respectively by experiments on the performance of each fusion methodanalysis, the experiments show that the image fusion method based on the secondaryfusion of multiple features of image fusion are valid.Finally, the use of the proposed new registration method and fusion methodcombining experiments were carried out and obtained the desired results. However,either method is not perfect, these methods also have their respective shortcomings.However, with the ongoing research of the integration of multi-source remote sensingimage based on the characteristics of class, I believe, continue to have more and betterintegration method appears, in order to compensate for this deficiency.