A Remote Sensing Image Fusion Algorithm Based on Nonnegative Ordinal Independent Component Analysis by Using Lagrange Algorithm

Abstract

Data fusion on remote sensing is one of important problems in current image processing. The key of a successful image fusion is to find an effective and practical image fusion algorithm. To eliminate high-order image data redundancy for two different remote sensing images which is nonnegative, a new approach using the nonnegative ordinal independent component analysis(ICA) based on Lagrange algorithm for remote image fusion between panchromatic and multi-spectral images is proposed. Firstly, the multi-spectral image and the panchromatic image are registered with the error in a pixel. Then the independent components, obtained by nonnegative ICA transform, are done factor analysis to determine the sequence of independent components successfully. Finally, the fused image is obtained by applying image fusion rules. Visual and statistical analyses prove that the concept of fusion method based on nonnegative ordinal ICA is promising, and it does significantly improve the fusion quality with higher signal-to-noise ratio compared to conventional IHS and wavelet fusion techniques.