Performance analysis of unsupervised unmixing models for thermal hyepsrpectral

摘要

Hyperspectral unmixing which consists of decomposing the measured pixel radiance into mixtures of ‘pure’ spectra whose fractions are referred to as abundances, is a common procedure in the signal and image applications. In this paper, the performance of unsupervised unmixing methods for hyperspectral data is evaluated using airborne thermal data and pixel-based ground truth in-situ. Specifically, the procedures of endmember extraction and mixing using linear and bilinear models are validated using set of pixels whose mixing proportion are known.