Bayesian Nonparametric Unmixing of Hyperspectral Images
This addresses the challenge of unknown endmember counts in hyperspectral imaging for remote sensing applications, offering an incremental improvement over existing methods.
The paper tackles the problem of hyperspectral unmixing (HSU) by jointly estimating the number of endmembers, their spectra, and abundances using a Bayesian nonparametric framework with an Indian Buffet Process prior, achieving results comparable to state-of-the-art methods while reliably inferring the number of endmembers, though it slightly overestimates in noisy scenarios.
Hyperspectral imaging is an important tool in remote sensing, allowing for accurate analysis of vast areas. Due to a low spatial resolution, a pixel of a hyperspectral image rarely represents a single material, but rather a mixture of different spectra. HSU aims at estimating the pure spectra present in the scene of interest, referred to as endmembers, and their fractions in each pixel, referred to as abundances. Today, many HSU algorithms have been proposed, based either on a geometrical or statistical model. While most methods assume that the number of endmembers present in the scene is known, there is only little work about estimating this number from the observed data. In this work, we propose a Bayesian nonparametric framework that jointly estimates the number of endmembers, the endmembers itself, and their abundances, by making use of the Indian Buffet Process as a prior for the endmembers. Simulation results and experiments on real data demonstrate the effectiveness of the proposed algorithm, yielding results comparable with state-of-the-art methods while being able to reliably infer the number of endmembers. In scenarios with strong noise, where other algorithms provide only poor results, the proposed approach tends to overestimate the number of endmembers slightly. The additional endmembers, however, often simply represent noisy replicas of present endmembers and could easily be merged in a post-processing step.