Variations on principal components and subspace projection for remote hyperspectral classification

This paper discusses recently developed algorithms for the classification of pixels in hyperspectral images, used in conjunction with a library of hyperspectral hemispherical reflectance data measured in the laboratory and partitioned into usable classes of materials. The algorithms are based upon functions of the principal components of the class covariances and the corresponding null spaces, and the underlying measures used in the classification statistics are similar to Mahalanobis distances. The algorithms can be used as stand-alone processing or combined with spatial and temporal algorithms in a higher level system of hyperspectral image processing. The nature of the classification algorithms and the database will be discussed, with particular attention being paid to issues specific to this approach. The basic performance of the classifier algorithms will be demonstrated using modified laboratory data. The applicability of Orthogonal Subspace Projection (OSP) methods to problems inherent in remote sensing using hyperspectral visible and infrared data will be emphasized, while specifically dealing with the compensation for inaccuracies in necessary estimates of atmospheric attenuation and target temperature. Preliminary results of classification of field collected hyperspectral data will also be presented, and ongoing and future work in hyperspectral classification described.