Hyperspectral target detection typically relies upon libraries of material reflectance and emissivity signatures. Application to real-world, airborne data requires estimation of atmospheric properties in order to convert reflectance/emissivity signatures to the sensor data domain. In the longwave infrared, an additional nuisance parameter of surface temperature exists that further complicates the signature conversion process. A significant amount of work has been done in atmospheric compensation and temperature-emissivity-separation techniques. This work examines the sensitivity of target detection performance for various materials with respect to target signature mismatch introduced from atmospheric compensation error or target temperature mismatch. Additionally, the impact of dimensionality reduction via principal components analysis is assessed.

Date Published: 5 May 2017
PDF: 12 pages
Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 1019809 (5 May 2017); doi: 10.1117/12.2262637

Published in SPIE Proceedings Vol. 10198:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII
Miguel Velez-Reyes; David W. Messinger, Editor(s)