Airborne Hyperspectral Imagery for Band Selection Using Moth-Flame Metaheuristic Optimization.

In this research, we study a new metaheuristic algorithm called Moth-Flame Optimization (MFO) for hyperspectral band selection. With the hundreds of highly correlated narrow spectral bands, the number of training samples required to train a statistical classifier is high. Thus, the problem is to select a subset of bands without compromising the classification accuracy.