Hyperspectral imagery is often visualized as a three-dimensional image cube (two spatial dimensions and one spectral). When a hyperspectral sensor is set to stare at a fixed location a fourth dimension (time) is created as each new cube is sampled in time. In a ground-based stare-mode geometry each new cube has near perfect spatial registration with the previous data cubes. The problem with standard spectral-only hyperspectral detection algorithms is that they do not make effective use of temporal information. In this paper we combine temporal-differencing with temporal-spectral detection algorithms. The temporal-differencing allows for removal of most of the background prior to temporal-spectral detection. The temporal-spectral approaches combine temporal information with standard spectral-only statistical methods. By combining temporal-differencing with temporal-spectral information we are able to significantly improve detector performance and reduce the false alarm rate. We demonstrate the performance of these methods using data from the FIRST (Field-Portable Imaging Radiometric Spectrometer Technology). All the computer simulations and field data experiments show that temporal-differencing improves performance, inclusion of temporal-spectral information improves performance, and that the combination of temporal-differencing with temporal-spectral information greatly improves performance.

Date Published: 27 April 2009
PDF: 9 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341U (27 April 2009); doi: 10.1117/12.816050

Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)