Dynamic hyperspectral embedding with a spatial sensitive graph

Graph embedding techniques are useful to characterize spectral signature relations for hyperspectral images. However, such images consists of disjoint classes due to spatial details that are often ignored by existing graph computing tools. Robust parameter estimation is a challenge for kernel functions that compute such graphs. Finding a corresponding high quality coordinate system to map signature relations remains an open research question. We answer positively on these challenges by proposing a kernel function of spatial and spectral information in computing neighborhood graphs. Furthermore, a multidimensional artificial field graph embedding technique that relies on simple additive assumptions of pair-dependent attraction and repulsion functions is proposed. High quality visualizations and improved classification performance demonstrate the benefits of the approach.