A Remote Sensing Image Fusion Algorithm Based on Constrained Nonnegative Matrix Factorization

Data fusion on remote sensing is one of important problems in current image processing. The key of a successful image fusion is to find effective and practical image fusion algorithm. To eliminate image data redundancy for two different remote sensing images, a new approach using the constrained nonnegative matrix factorization for remote image fusion between Landsat ETM+ panchromatic and CBERS multi-spectral images is proposed. Visual and statistical analyses prove that the concept of fusion based on constrained nonnegative matrix factorization is promising, and it does significantly increasing the signal-to-noise ratio and improve the fusion quality compared to conventional IHS and wavelet fusion techniques.