Evaluation of different fitness functions integrated with genetic algorithm on unsupervised classification of satellite images

摘要

In traditional unsupervised classification method, the number of clusters usually needs to be assigned subjectively by analysts, but in fact, in most situations, the prior knowledge of the research subject is difficult to acquire, so the suitable and best cluster numbers are very difficult to define. Therefore, in this research, an effective heuristic unsupervised classification method—Genetic Algorithm (GA) is introduced and tested here, because it can be through the mathematical model and calculating procedure of optimization to determine the best cluster numbers and centers automatically. Furthermore, two well-known models--Davies-Bouldin's and the K-Means algorithm, which adopted by most research for the applications in pattern classification, are integrated with GA as the fitness functions. In a word, in this research, a heuristic method—Genetic Algorithm (GA), is adopted and integrated with two different indices as the fitness functions to automatically interpret the clusters of satellite images for unsupervised classification. The classification results were compared to conventional ISODATA results, and to ground truth information derived from a topographic map for the estimation of classification accuracy. All image-processing program is developed in MATLAB, and the GA unsupervised classifier is tested on several image examples.