A Quantum-Modeled Fuzzy C-Means clustering algorithm for remotely sensed multi-band image segmentation

A Quantum-Modeled Fuzzy C-Means clustering algorithm for remotely sensed multi-band image segmentation is explored and evaluated. Data sets of interest include remotely sensed multi-band 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, in general, the solution is enhanced via introduction of the quantum state machine in and of itself, which provides random fuzzy membership input to the Fuzzy C-Means soft partitioning algorithm, while the addition of quantum operators provide additional contributions to solution diversity. Typically, when evaluated for cluster validity, the algorithm has shown to produce effective solutions.