Coupled non-negative matrix factorization (CNMF) for hyperspectral and multispectral data fusion: Application to pasture classification

摘要

Coupled non-negative matrix factorization (CNMF) is introduced for hyperspectral and multispectral data fusion. The CNMF fused data have little spectral distortion while enhancing spatial resolution of all hyperspectral band images owing to its unmixing based algorithm. CNMF is applied to the synthetic dataset generated from real airborne hyperspectral data taken over pasture area. The spectral quality of fused data is evaluated by the classification accuracy of pasture types. The experiment result shows that CNMF enables accurate identification and classification of observed materials at fine spatial resolution.