Computationally Efficient Hyperspectral Data Learning Based on the Doubly Stochastic Dirichlet Process

Abstract

The Dirichlet process (DP) prior is effective in modeling HSIs (HSI) and identifying land-cover classes. However, modeling a continuously varying intensity of these land covers elegantly and consistently is still a challenge. We propose a doubly stochastic DP (DSDP) as an efficient model of the global topic measurement space, which imposes a weaker assumption compared with the discrete Markov assumption, resulting in a lower computational cost than other DP-prior-based models. We also present a mixture model of DSDP, which is termed the marked sigmoidal Gaussian process (SGP) DSDP mixture model. It can be thinned from a DP mixture without massive auxiliary covariates, and the marked function prior makes the number of land-cover classes consistent, whereas the SGP function prior models the HSI land-cover variation globally. The consistency of the number of land covers is maintained for various HSIs with large-scale geographical areas. Experiments show that the model is robust and consistent on HSI identification with weak or even no supervision.