The Study On Multi-sensor Image Fusion In Remote Sensing

Multi-source remote-sensor image fusion is an important and useful technique not only for data fusion but also image analysis and computer vision. Study and application of image fusion have aimed to combine multiple source information from various sensors intelligently and obtain more detailed, complete, dependable description than a single sensor. Our paper is mainly focused in multi-source remote sensors image fusion. On the aspects of pixel level fusion, feature level fusion and fast fusion, some effective methods are proposed by studying multi-resolution analysis theory and neural network deeply.Although the wavelet transform theory provides a good framework for signal analysis, the disadvantage of existing Mallat fusion algorithm for without enough fused image detail components and ringing effect limits its applications greatly, a new method of high fidelity based on wavelet transform (HFWT) is proposed. Experiments verify that new approach can effectively reduce distortion by merging the high-resolution panchromatic image approximation into the multi-spectral image approximation using gray factor compared with the existing Mallat fusion algorithm.Due to the reason that single wavelet transform makes some spectral aberrance, a new method is developed based on multi-wavelet transform (MWT). Experiments statistical results verify that new approach can effectively avoid the spectral aberrance by means of shift-invariant multiple wavelets decomposition and the ringing effect from the final merged image reconstructed by single wavelet transform approach.Wavelet transform only decomposes the low frequency components of the images, while not its high frequency components. In fact, the high frequency components of the images contain much detail information. To tackle this problem, a new method of wavelet packet transform orientation adaptive (WPT-OA) is proposed. Visual result and statistical parameters show that the performance of our approach is more efficient than that of WT-based one. It not only greatly increases spatial detail information of the image, but also well preserves the spectral information of the image.The WPT-OA approach can not meet the requirement of real time computation, especially the computation cost is exponentially increasing with the growth of the decomposition level by WPT, having learned the flow of the wavelet packet and analyzing its parallelism, we propose and realize an efficient solution of parallel image fusion based on the WPT-OA. Based on computation local characteristic and boundary relativity of wavelet transform, a new idea, the regular data blocking and redundant storage is proposed. No communication is needed during the fusion. Under different conditions as different image size and different cluster size, On a 8 computers environment of Pentium PC and 1000Mbps Ethernet, the experimental result shows that the efficiency of parallel computation is obviously improved and accelerated ratio can be as high as 6.3798.Region and edge are the basic features of object description. In order to settle the linear target extraction based on feature-level image fusion, a new target extract approach is proposed based on the integration regions from SAR image with edges from optical image. During the course of region segmentation, it is proved that the texture inertial moment is the best operator in feature vector sets, and fuzzy neural network clustering method based on Mahalanobis distance of feature sets is highly effective by experiment. The final result shows that the fused image has exact target region and precise edge.