Research On Remote Sensing Image Fusion Algorithm

With the development of the remote sensing technology, multi-source remote sensing image fusion technology has been concerned and applied widely. Its theories and algorithms have become the focus of research in the field of remote sensing. Multi-source remote sensing image fusion technology makes preprocessing about panchromatic(Pan) image and multispectral(Ms) image which have different space resolution and spectral resolution information obtained by different sensors in the same area. Then it combines the images through certain algorithms according to the characteristics of the image data to get an Ms image with high spatial resolution and high spectral resolution. Multi-sensor image fusion overcomes the difference and limitation of the single sensor image in spectrum and space resolution etc. At present, remote sensing image fusion has been widely used in cartography, vegetation monitoring, geological disaster monitoring and other fields.This paper firstly introduces the background and significance of remote sensing image fusion, and analysis the theory of remote sensing image fusion. It studies several classical remote sensing image fusions, such as intensity-hue-saturation (IHS)method and its improved methods, and also Brovey transform(BT), Principal component analysis (PCA) methods. On the basis of the IHS method and its improved methods (mostly AIHS and IAIHS), this paper proposes algorithm about improved adaptive IHS method. Firstly a modulation parameter t is used to balance the spatial resolution and spectral resolution of the fusion image. Secondly using the adaptive parameter a to further improve the algorithm's adaptability. Finally, the redundancy of the algorithm is analyzed to optimize the algorithm and improve the running speed. The improved algorithm is not strict with the image preprocessing and registration accuracy. At the same time, the edge of the original Pan image and Ms image are weighted. The improved algorithm has both advantages of the pixel level remote sensing image fusion and feature level remote sensing image fusion, and reduces the image data processing capacity and the complexity of system. It has a good practicability. The improved algorithm and existing algorithms are tested and verified by three different satellite data. Experiments showed that the improved method can improve spectral quality and maintain spatial resolution compared with the AIHS and IAIHS methods.