Regional and Entropy component analysis based remote sensing images fusion

In this paper a new regional and entropy component analysis based fusion approach is proposed for multispectral and panchromatic images fusion. The input images are decomposed into low frequency subband and high frequency subbands by stationary wavelet transform. The low frequency subband coefficients of multispectral image are selected as those of fused image. The fused rule of high frequency subbands are designed according to the similarity of corresponding region. The similar corresponding regions are fused by magnitude maximum rule, otherwise, fused by statistical model method. In order to obtain the corresponding region, the input images are divided into windows and extracted the integrate features from panchromatic and multispectral images. Inspired by the kernel entropy component analysis, the linear entropy component analysis (ECA) is proposed and used to extract the spectral feature. Different from traditional regional fusion approaches dividing input images separately, ours is generated from the synthetic features. The region result can be gotten by feature clustering using Fuzzy C-means, which is mapped into each of high frequency subbands. Some experiments are taken on some remote sensing images, and the results show the superiorities of our proposed method, both in subjective evaluation and some numerical guidelines