Hyperspectral band selection through Optimum-Path Forest and evolutionary-based algorithms

In this paper we addressed the problem of dimensionality reduction in hyperspectral imagery classification by combining OPF classifier together with three recent evolutionary-based optimization algorithms: PSO, HS and GSA. We conducted experiments with two public datasets (Indian Pines and Salinas), which demonstrated that OPF combined with HS and GSA have obtained promising results, being the former the fastest approach. In regard to Indian Pines dataset, HS and GSA have achieved close classification rates, but HS has selected 46.25% less bands, which means a faster feature extraction step. For future works, we intend to provide a more detailed convergence analysis for PSO, HS and GSA, and also to introduce novel evolutionary-based band selection techniques and also to apply these methodologies for hyperspectral image classification in forest and agriculture applications.