Multidomain Subspace Classification for Hyperspectral Images

Abstract

Hyperspectral imaging offers new opportunities for pattern recognition tasks in the remote sensing community through its improved discrimination in the spectral domain. However, such advanced image processing also brings new challenges due to the high data dimensionality in both the spatial and spectral domains. To relieve this issue, in this paper, we present a novel multidomain subspace (MDS) feature representation and classification method for hyperspectral images. The proposed method is based on a patch alignment framework. In order to optimally combine the feature representations from the various domains and simultaneously enhance the subspace discriminability, we incorporate the supervised label information into each domain and further generalize the framework to a multidomain version. Furthermore, we develop an iterative approach to alternately optimize the MDS objective function by considering it as two subconvex optimizations. The classification performance on three standard hyperspectral remote sensing images confirms the superiority of the proposed MDS algorithm over the state-of-the-art subspace learning methods.