Tech Report: A Homogeneity-Based Multiscale Hyperspectral Image Representation for Sparse Spectral Unmixing

TL;DR

This paper introduces a multiscale hyperspectral image representation method based on homogeneity testing and superpixels, enhancing sparse spectral unmixing accuracy and efficiency, especially under noisy conditions.

Contribution

It presents a novel hierarchical superpixel segmentation approach that improves spectral unmixing by incorporating spatial regularity with low computational cost.

Findings

High-quality abundance estimation demonstrated in simulations.

Method is computationally efficient, suitable for noisy data.

Improves unmixing accuracy by leveraging multiscale spatial information.

Abstract

Several approaches have been proposed to solve the spectral unmixing problem in hyperspectral image analysis. Among them the use of sparse regression techniques aims to characterize the abundances in pixels based on a large library of spectral signatures known a priori. Recently, the integration of image spatial-contextual information significantly enhanced the performance of sparse unmixing. In this work, we propose a computationally efficient multiscale representation method for hyperspectral data adapted to the unmixing problem. The proposed method is based on a hierarchical extension of the SLIC oversegmentation algorithm constructed using a robust homogeneity testing. The image is subdivided into a set of spectrally homogeneous regions formed by pixels with similar characteristics (superpixels). This representation is then used to provide prior spatial regularity information for the abundances of materials present in the scene, improving the conditioning of the unmixing problem. Simulation results illustrate that the method is capable of estimating abundances with high quality and low computational cost, especially in noisy scenarios.