Joint nonnegative matrix factorization for hyperspectral and multispectral remote sensing data fusion

摘要

This paper presents a new fusion approach producing unobservable fused remote sensing data with high spatial and spectral resolutions. This approach, related to linear spectral unmixing (LSU) techniques, introduces joint nonnegative matrix factorization (JNMF) for combining observable low spatial resolution hyperspectral and high spatial resolution multispectral data. JNMF is applied to synthetic but realistic data generated from real airborne hyperspectral data. Spectral and spatial qualities of fused data are evaluated by frequently used criteria. Experimental results show the low computational cost of the proposed approach, and the good spectral and spatial fidelities of the fused data. Our method also outperforms the recently proposed coupled nonnegative matrix factorization (CNMF) method.