Hyperspectral and multispectral data fusion based on nonlinear unmixing

Data fusion of low spatial-resolution hyperspectral (HS) and high spatial-resolution multispectral (MS) images based on a linear mixing model (LMM) enables the production of high spatial-resolution HS data with small spectral distortion. This paper extends the LMM based HS-MS data fusion to nonlinear mixing model using a bilinear mixing model (BMM), which considers second scattering of photons between two distinct materials. A generalized bilinear model (GBM) is able to deal with the underlying assumptions in the BMM. The GBM is applied to HS-MS data fusion to produce high-quality fused data regarding multiple scattering effect. Semi-nonnegative matrix factorization (Semi-NMF), which can be easily incorporated with the existing LMM based fusion method, is introduced as a new optimization method for the GBM unmixing. Comparing with the LMM based HS-MS data fusion, the proposed method showed better results on synthetic datasets.