Supervised unconstrained and constrained least squares unmixing in hyperspectral imagery

Daniel C. Heinz; Thomas Bahr; Greg Terrie

doi:10.1117/12.2584118

12 April 2021 Supervised unconstrained and constrained least squares unmixing in hyperspectral imagery

Daniel C. Heinz, Thomas Bahr, Greg Terrie

Proceedings Volume 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII; 1172710 (2021) https://doi.org/10.1117/12.2584118
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Spectral mixture analysis of hyperspectral imagery allows for detection, classification and quantification of targets present in the imaged scene. It can be difficult to characterize the performance of spectral unmixing techniques at detection, classification and quantification of field data at the subpixel level due to limited ground truth, especially for mixed pixels. Fortunately, the SpecTIR Hyperspectral Airborne Experiment (SHARE) 2012 contains a set of targets specifically designed to test spectral unmixing algorithms. In this paper we explore the performance of an unconstrained and three constrained least squares methods for supervised spectral unmixing. Each of the three methods provides an estimate of the abundance of known targets which can be used for detection, classification and quantification. A detailed evaluation of these spectral unmixing techniques on the SHARE 2012 hyperspectral data is used to demonstrate the performance of each method at supervised target detection, classification and quantification.

Conference Presentation

Citation Download Citation

Daniel C. Heinz, Thomas Bahr, and Greg Terrie "Supervised unconstrained and constrained least squares unmixing in hyperspectral imagery", Proc. SPIE 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, 1172710 (12 April 2021); https://doi.org/10.1117/12.2584118

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