Study of Remote Sensing Image Fusion and Its Application in Image Classification

Data fusion is a formal framework in which is expressed means and tools for the alliance of data originating from different sources. It aims at obtaining information of greater quality; the exact definition of ‘greater quality’ will depend upon the application. Satellites remote sensing image fusion has been a hot research topic of remote sensing image processing. Make Multispectral images matching with TM panchromatic image, and the error control in 0.3 pixels within. Use Smoothing Filter-based Intensity Modulation (SFIM), High Pass Filter (HPF), Modified Brovery, Multiplication, IHS, Principle component analysis Transform (PCA) methods for the fusion experiment. Use some parameters to evaluate the quality of fused images. Select representative features from the fused and original images and analysis the impact of fusion method. The result reveals that all the six methods have spectral distortion, HPF and SFIM are the best two in retaining spectral information of original images, but the PCA is the worst. In the process of remote sensing image data fusion, different method has different impact on the fused images. Use supervised classification and unsupervised classification method to make image classification experiments, the study reveals that all the fused images have higher spatial frequency information than the original images, and SFIM transform is the best method in retaining spectral information of original image. *Corresponding author: Yao Jing, School Of Geomatics,Liaoning Technical University , 123000, 359 mailbox,Zhonghua street,Fuxin,China. [email protected], telephone number:13941830365