Wavelet-based Remote Sensing Image Fusion with PCA and Feature Product

Abstract

Image fusion is one of the important techniques for image information enhancing. In order to utilize respective information from different remote sensing images, we propose a new image fusion method based on the Principal Component Analysis (PCA) and feature product of wavelet transform. Firstly, the multi-spectral image is transformed with PCA. Secondly, the histogram-matched panchromatic image and the first principal component are decomposed into wavelet coefficients respectively. Thirdly, the first principal component of the multi-spectral image and the panchromatic image are merged with feature product of wavelet based fusion method, and the former is replaced with the merged data. Finally, the new multi-spectral image is obtained by inverse PCA. Some evaluation parameters are suggested and applied to compare the new method with those of PCA method, the combined PCA and traditional wavelet method and the combined PCA and local deviation of wavelet method. Subjective visual effect and objective statistical results indicate that the performance of the new method is better than those methods. It not only preserves spectral information of the original multi-spectral image well, but also enhances spatial detail information greatly.