Manifold alignment for multitemporal hyperspectral image classification

While spectral and temporal advantages of multitemporal hyperspectral images provide opportunities for advancing classification of time varying phenomena, significant challenges are associated with high dimensionality and nonstationary signatures. While manifold learning retains critical geometry and develops a low dimension space where class clusters are recovered, spectral changes in temporal imagery impact the fidelity of the geometric representation of class dependent data. In this paper, we investigate a manifold alignment framework that exploits prior information while exploring similar local structures. The aim is to make use of common underlying geometries of two multitemporal images and embed the resemblances in a joint data manifold for classification tasks. Promising results support the advantages of the proposed manifold alignment approach.