Bayesian Nonparametric Unmixing of Hyperspectral Images

TL;DR

This paper introduces a Bayesian nonparametric method for hyperspectral unmixing that automatically estimates the number of endmembers, their spectra, and abundances, demonstrating effectiveness on real and simulated data.

Contribution

It proposes a novel Bayesian nonparametric framework using the Indian Buffet Process to jointly estimate endmembers, their number, and abundances, advancing beyond methods assuming known endmember count.

Findings

Effectively estimates the number of endmembers from data.

Performs comparably to state-of-the-art methods on real datasets.

Slight overestimation of endmembers in noisy scenarios, which can be refined post-processing.

Abstract

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.