Improving the Dynamic Clustering of Hyperspectral Data Based on the Integration of Swarm Optimization and Decision Analysis

Unsupervised or clustering algorithms can be considered to overcome the need for both high-quantity and high-quality training data for hyperspectral data classification. One of the most widely used algorithms for the clustering of remotely-sensed data is partitional clustering. Partitional clustering is affected by 1) the optimal number of clusters (NOC), 2) the position of cluster centers in hyper-dimension space, and 3) a set of optimally discriminating spectral bands. Among these three parameters, the NOC and their positions can be found simultaneously by dynamic clustering approaches. In this paper, an innovative two-stage dynamic clustering method is proposed and evaluated. In the first stage, the optimum set of solutions is achieved by a multi-objective particle swarm optimization. Then, using an efficient multi-criteria decision-making method, namely, the technique for order of preference by similarity to ideal solution (TOPSIS), a ranking is done among the optimal set of solutions to select the best one. Comparisons with some classic algorithms reveal that the proposed method is more effective at detecting the optimal number and position of clusters. In addition, the proposed algorithm generates better clustering results for hyperspectral data. Indeed, our method leads to a 5%-10% improvement upon classification accuracy.