Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion

TLDR

The study proposes CNMF unmixing to fuse low‑resolution hyperspectral with high‑resolution multispectral imagery, yielding data with enhanced spatial and spectral resolution. CNMF alternately unmixed hyperspectral and multispectral data into end‑member and abundance matrices using a linear spectral mixture model, with sensor observation models incorporated into the initialization matrices. The algorithm is simple to implement and, in simulations, produces high‑quality fused data that improves material identification and classification at high spatial resolution.

Abstract

Coupled nonnegative matrix factorization (CNMF) unmixing is proposed for the fusion of low-spatial-resolution hyperspectral and high-spatial-resolution multispectral data to produce fused data with high spatial and spectral resolutions. Both hyperspectral and multispectral data are alternately unmixed into end member and abundance matrices by the CNMF algorithm based on a linear spectral mixture model. Sensor observation models that relate the two data are built into the initialization matrix of each NMF unmixing procedure. This algorithm is physically straightforward and easy to implement owing to its simple update rules. Simulations with various image data sets demonstrate that the CNMF algorithm can produce high-quality fused data both in terms of spatial and spectral domains, which contributes to the accurate identification and classification of materials observed at a high spatial resolution.