Identifiability of geometric models for linear unmixing at different spatial resolutions in hyperspectral unmixing

Proposed and existing hyperspectral remote sensors, provide information about the scene of interest at resolutions ranging from few meters to few kilometers in terrestrial and space applications. Understanding the type of information extracted with image exploitation algorithms and how does it relates to actual spectra on the ground are important problems when we look into algorithms that perform unmixing of hyperspectral images for subpixel analysis. In this paper, we investigate how spatial resolution affects the capability of unmixing algorithms based on geometric models to extract information from a scene. We study the performance of the positive matrix factorization for unmixing of hyperspectral at different spatial resolutions and how does it compare with other approaches such as MaxD, and SMACC. Hyperspectral imagery collected using the AISA sensor at 1m and 4m are used for the experiments. The results obtained illustrate some of the effects that algorithms assumptions have on unmixing results.