Characterizing mixed pixels is an important topic in the analysis of hyperspectral data. Recently, a subspacebased technique in a multinomial logistic regression (MLR) framework called MLRsub has been developed to address this issue. MLRsub assumes that the training samples of each class live in a single low-dimensional subspace. However, having in mind that materials in a given class tend to appear in groups and the (possible) presence on nonlinear mixing phenomena, a more
powerfull model is a union of subspaces. This paper presents
a new approach based on union of subspaces for hyperspectral
images. The proposed method integrates subspace clustering with
MLR method for supervised classification. Our experimental
results with an urban hyperspectral image collected by the NSFfunded
Center for Airborne Laser Mapping (NCALM) over the
University of Houston campus indicate that the proposed method
exhibits state-of-the-art classification performance.