Game theory models for spectral band grouping and classifier ensembles for hyperspectral image classification

摘要

This paper investigates the utilization of game theory models for automated analysis of hyperspectral imagery. The author proposes three approaches to using strategic, competitive game theory models for groundcover classification using hyperspectral imagery, including the application of game theory models to (i) hyperspectral band grouping and (ii) pixel classification in a classifier ensemble system. Proposed model (i) uses conflict data filtering based on mutual entropy along with the Nash equilibrium as the means to find a steady state solution. Proposed model (ii) utilizes a strategic coalition game, specifically the weighted majority game (WMG). Both a models are implemented under the assumption that all players are rational. The author incorporates each of the proposed approaches, (i) and (ii), into a multi-classifier decision fusion (MCDF) system for automated ground cover classification with hyperspectral imagery. The paper provides experimental results demonstrating the efficacy of the proposed game theoretic approaches, presenting significant improvements over existing methods.