I’ve been implementing several hyperspectral target detection algorithms for the Matlab Hyperspectral Toolbox. I am going to use this post to summarize my research and implementations. It will take me a few days to get all my thoughts together, so this note will be a “living post” over the next week or so. Please email me if you find any errors or something is not explained clearly. I can be contacted at first.last-At-gergltd.com. Thanks, Isaac Gerg
– Number of bands. E.g. for AVIRIS p = 224.
– Number of pixels.
– Number of materials in the scene.
– Matrix of hyperspectral imagery (HSI) data. Size is (p x N).
– Observation vector, a pixel. Size is (p x 1).
– Data mean. Size is (p x 1).
– Target of interest. Size is (p x 1).
– Covariance matrix of M. Size is (p x p).
– Correlation matrix of M. Size is (p x p).
– Matrix of background endmembers. Size is (p x q).
– Orthogonal projection of .
Assume the mean has been removed from the data unless otherwise noted. Assume these kernels work on radiance or reflectance data unless otherwise noted.
Matched Filter (MF)
Adaptive Coherent/Cosine Estimator (ACE)
Constrained Energy Minimization (CEM)
and are not centered for this algorithm. I.e. Do not remove the mean of the data when computing this kernel.
Generalized Likelihood Ratio Test (GLRT)
Orthogonal Subspace Projection (OSP)
Adaptive Matched Subspace Detector (AMDS)
B is a matrix of background signatures. Size is (p x q). Z is a matrix of background and target signatures. Size is (p x (q + # targets)).
More to come…..