Research On Multi-Source Image Fusion And Its Applications

Various image sensors appear and their acquired data become explosive and multifarious with the fast development of micro-electronics. In this situation, traditional information processing cannot meet requirements and information fusion rises. The concept of information fusion indicates the information processing and synthesization of multilevel, multiaspect and multilayer. Multi-source image fusion is derived from information fusion and it includes digital image processing, computer science and artificial intelligence and so on. Image fusion is implemented by integrating multiple source images with redundant and complementary information into one result with better intelligibility and definition. In this way, redundancy can be reduced, while complementary information can be utilized more effectively.This thesis studies the research backgrounds of multi-source image fusion and its significances, basic theories and systematic constructure of image fusion. Then we review the achievements of image fusion research at home and abroad. Following that, we set to our works on the deficiencies of existed works. Detailed contents consist of the reviewal and novel idea about multiresolution based image fusion, explorations in image fusion using PDEs, investigations into combination of multiresolution analysis and region segmentation, objective evaluations of various fusion methods and the applications of feature level fusion, such as road object recognition in high resolution multispectral images. The main work and innovations are listed as follows:1. Based on a reviewal and summarization of existed image fusion methods which utilize multiresolution analysis, we propose a novel biorthogonal multiwavelet based multispectral and PAN image fusion algorithm. Increased number of basis in multiwavelet can handle the tradeoff between symmetry and orthogonality, filter length and vanishing moment. Meanwhile, combination of average and selection is used in the fusion scheme for preserving the salient information of scale coefficients from sources.2. We propose a salience preserving based first-order contrast multi-focus image fusion method. According to the different importance of each source, we set different weights to each pixel in different source. Then weighted contrasts of sources are integrated into the target gradient of fused result. Moreover, we extend it into color domain by performing importance weight based color channel combination.3. In image fusion, people pay more attentions to real object or region within sources, but not single pixels. Here we fuse optical and IR images by combing multiresolution analysis and region segmentation. Hereinto, match measure is utilized as the decision rules of wavelet coefficients. Meanwhile, we give a detailed analysis of the framework of multiresolution and region based feature level fusion, impacts of uni-modal and joint segmentation for fusion, selection of fusion rules.4. We set forth most existed objective evaluation methods and how to adopt the objective evaluation method. An objective evaluation method based on structure similarity between sources and fused result is presented. The evaluation method takes the similarity of mean, variance and correlation into accounts and evaluates the fusion method from the viewpoint of gray values and gradients.5. An automatic road recognition method in low resolution remote sensing image is proposed here. Hereinto, perceptual grouping is introduced to decide the road edge line group after edge detection and redundant line segments exclusion. Road seeds are generated accorading to candidate road edge line groups. Then road network is delineated by dynamic programming based road tracking. Finally false road segments are eliminated by knowledge ruling.6. Road object has more traits in high resolution remote sensing images. It includes not only the topology, but also the spectral and shape traits and so on. Thus we proposed a road network recognition method, which combines both edge detection and spectral classification. Bacause roads in high resolution images can be modeled as image blocks with same statistic characteristics and these blocks can be acquired by classification.