Fusion of remote sensing data of the Changbai Mountain area based on the Principal Component and Wavelet Transformation

Abstract

The resource of remote sensing data is rich, their formats and resolution are very different, it is necessary to take some approach to fuse the data. The technique of Principal Components Transform fuses data by replacing the first principal component with the high resolution image after the principal component analysis of multi-spectrum image and then carry on the Principal Components Inverse Transformation to obtain the fusion image; The Wavelet Transformation chooses high- frequency and low-frequency to carry on inverse transformation after decomposing the panchromatic image and multi-spectra image with some wavelet. In this paper, unify the 2 ways to fuse data, the first step is to get the principal components of the IKONOS's multi-spectral image of ChingBai Mountain Area, By the way of Wavelet Decomposition, it is easy to get the high-frequency and low-frequency of the principal components and by taking the method of Wavelet Reconstruction, it is useful to reconstruct the principal components with high-frequency of the panchromatic image of IKONOS and the low-frequency of Multi-spectral image; Then obtain the final fusion image by taking the method of Principal Component Inverse Transform; Combining with the way of visual interpretation and the rule of information entropy and spectral correlation coefficient, we get a good result, the amount of fusion image's information is 7.4875, and the correlation coefficient is 0.8619. Compared to a single method, the spectral information and resolution have been improved; the result shows that, the method of combining with Wavelet Transform and Principal Component Transform merge their own advantages and make up the disadvantages, the fusion image not only improves the spatial resolution of the original image, but also retains the relatively high spectral resolution, it will be propitious to the further extraction and processing of the remote sensing data.