A statistics-based IHS-wavelet method for BJ-1 microsatellite image fusion

Abstract

The difficulties of BJ-1 image fusion are the big resolution gap (up to 1:8) between the multispectral and panchromatic image and the task to seek a balance between high spatial resolution and the least spectral distortion. In this paper, an experiment has been accomplished prior to the determination of fusion models with a conclusion of the close relation between approximation and spectral distortion. Hence, a combination method of IHS and wavelet decomposition is proposed, of which an optimal fusion model based on spectral and spatial statistical indexes is designed for approximation coefficients in an effort to compromise between high spectral preservation (low distortion) and spatial definition. As to the detail coefficients, a set of multi-scale diverse local algorithm inspired by some successful ARSIS models is employed, which includes the adjustment and establishment of relationship between components from multispectral and panchromatic wavelet decomposition. The fusion results are subsequently compared with counterparts of other methods such as IHS, wavelet and PCA. Several evaluating indicators are employed to give quantitative assessment to the fusion performance. Among all the fusion methods, the statistics-based IHS-Wavelet method fusion demonstrates the most satisfactory spatial definition while keeping the least spectral distortion.