Remote sensing image data fusion based on local deviation of wavelet packet transform

The goal of image fusion is to create new images that are more suitable for the purposes of human visual perception, machine vision, object detection and target recognition. In order to adequately make use of all kinds of remote sensing images information, a new image fusion method based on local deviation of wavelet packet transform is proposed. In the fusion processing, the fused approximate coefficients are obtained by weighted average method. For the bigger local deviation of the each decomposed approximate coefficient, we choose a big power gene. The other approximate coefficient chooses a small one. The fused detailed coefficients are obtained by setting each coefficient equal to the corresponding input image wavelet packet coefficient that has the greatest local deviation. Both the image information entropy and the image clarity are employed to evaluate the performances of remote sensing fusion algorithm. The experimental results show that the proposed method is more effective than the other methods.