Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification using Markov Random Fields

TL;DR

This paper introduces spectral angle based unary energy functions for hyperspectral image classification using Markov Random Fields, demonstrating improved or comparable accuracy with reduced computational time.

Contribution

Proposes two novel spectral angle based unary energy functions for spatial-spectral hyperspectral classification with Markov Random Fields, improving efficiency and accuracy.

Findings

Minimum spectral angle unary energy yields better or comparable results.

Proposed methods outperform state-of-the-art in accuracy and speed.

Experiments on two datasets validate effectiveness.

Abstract

In this paper, we propose and compare two spectral angle based approaches for spatial-spectral classification. Our methods use the spectral angle to generate unary energies in a grid-structured Markov random field defined over the pixel labels of a hyperspectral image. The first approach is to use the exponential spectral angle mapper (ESAM) kernel/covariance function, a spectral angle based function, with the support vector machine and the Gaussian process classifier. The second approach is to directly use the minimum spectral angle between the test pixel and the training pixels as the unary energy. We compare the proposed methods with the state-of-the-art Markov random field methods that use support vector machines and Gaussian processes with squared exponential kernel/covariance function. In our experiments with two datasets, it is seen that using minimum spectral angle as unary energy produces better or comparable results to the existing methods at a smaller running time.