Subspace methods for hyperspectral imagery enable detection and identification of targets under unknown
environmental conditions (i.e., atmospheric, illumination, surface temperature, etc.) by specifying a subspace of possible
target spectral signatures (and, optionally, a background subspace) and identifying closely fitting spectra in the image.
The subspaces, defined from a set of exemplar spectra, are compactly expanded in singular value decomposition basis
vectors or, less commonly, endmember basis spectra, linear combinations of which are used to fit the image data. In the
present study we compared detection performance in the thermal infrared using several different constrained and
unconstrained basis set expansions of low-dimensional subspaces, including a method based on the Sequential
Maximum Angle Convex Cone (SMACC) endmember algorithm. Constrained expansions were found to provide a
modest improvement in algorithm robustness in our test cases.