A Quantum-Modeled Artificial Bee Colony clustering algorithm for remotely sensed multi-band image segmentation

Abstract

A Quantum-Modeled Artificial Bee Colony clustering algorithm for remotely sensed multi-band image segmentation is explored and evaluated. Data sets of interest include remotely sensed multi-band RGB imagery, which subsequent to classification is analyzed and assessed for accuracy. Results demonstrate that the algorithm exhibits improved accuracy, when compared to its classical counterpart. Moreover, solutions are enhanced via introduction of the quantum state machine, which provides random initial food sources and variables as input to the Artificial Bee Colony algorithm, and quantum operators, which bring about convergence and maximize local search space exploration. Typically, the algorithm has shown to produce better solutions.