Multiplicative Iterative Nonlinear Constrained Coupled Non-negative Matrix Factorization (MINC-CNMF) for Hyperspectral and Multispectral Image Fusion

Abstract

Hyperspectral and Multispectral (HS-MS) image fusion is a most trending technology that enhance the quality of hyperspectral image. By this technology, retrieve the precise information from both HS and MS images combined together increase spatial and spectral quality of the image. In the past decades, many image fusion techniques have been introduced in literature. Most of them using Coupled Nonnegative matrix factorization (CNMF) technique which is based on Linear Mixing Model (LMM) which neglect the nonlinearity factors in the unmixing and fusion technique of the hyperspectral images. To overcome this limitation, we are going to propose an unmixing based fusion algorithm namely Multiplicative Iterative Nonlinear Constrained Coupled Nonnegative Matrix Factorization (MINC-CNMF) that enhance the spatial quality of the image by considering the nonlinearity factor associated with the unmixing process of in the image. This method not only consider the spatial quality but also enhance the spectral data by imposing constraints known as minimum volume (MV) which helps to estimate accurate endmembers. We also measure the strength and superiority of our method against baseline methods by using four public dataset and found that our method shows outstanding performance than all the baseline methods.