In this paper we propose an ι₁-norm penalized sparse support vector machine (SSVM) as an embedded approach to the hyperspectral imagery band selection problem. SSVMs exhibit a model structure that includes a clearly identifiable gap between zero and non-zero weights that permits important bands to be definitively selected in conjunction with the classification problem. The SSVM Algorithm is trained using bootstrap aggregating to obtain a sample of SSVM models to reduce variability in the band selection process. This preliminary sample approach for band selection is followed by a secondary band selection which involves retraining the SSVM to further reduce the set of bands retained. We propose and compare three adaptations of the SSVM band selection algorithm for the multiclass problem. Two extensions of the SSVM Algorithm are based on pairwise band selection between classes. Their performance is validated by using one-against-one (OAO) SSVMs. The third proposed method is a combination of the filter band selection method WaLuMI in sequence with the (OAO) SSVM embedded band selection algorithm. We illustrate the perfomance of these methods on the AVIRIS Indian Pines data set and compare the results to other techniques in the literature. Additionally we illustrate the SSVM Algorithm on the Long-Wavelength Infrared (LWIR) data set consisting of hyperspectral videos of chemical plumes.

Date Published: 13 June 2014
PDF: 15 pages
Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 90881F (13 June 2014); doi: 10.1117/12.2063812

Published in SPIE Proceedings Vol. 9088:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX
Miguel Velez-Reyes; Fred A. Kruse, Editor(s)