Hyperspectral Image Classification Using Spectral-Spatial Dual Random Fields With Gaussian and Markov Processes

Abstract

This article presents a novel hyperspectral image (HSI) classification approach that integrates the sparse inducing variational Gaussian process (SIVGP) with a spatially adaptive Markov random field (SAMRF), termed G-MDRF. Variational inference is employed to obtain a sparse approximation of the posterior distribution, modeling the spectral field within the latent function space. Subsequently, SAMRF is utilized to model the spatial prior within the function space, while the alternating direction method of multipliers (ADMM) is employed to enhance computational efficiency. Experimental results on three datasets with varying complexity show that the proposed algorithm improves computational efficiency by approximately 152 times and accuracy by about 7%–26% compared to the current popular Gaussian process methods. Compared to classical random field methods, G-MDRF rapidly achieves a convergent solution with only one ten-thousandth to one hundred-thousandth of the iterations, improving accuracy by about 5%–18%. Particularly, when the number of classes in the dataset increases and the scene becomes more complex, the proposed method demonstrates a greater advantage in both computational efficiency and classification accuracy compared to existing methods.