Combining Fuzzy Probability and Fuzzy Clustering for Multispectral Satellite Imagery Classification

Abstract

In practice, the classification algorithms and the initialization of the clusters and the initial centroid of clusters have great influence on the stability of the algorithms, dealing time and classification results. Some algorithms are used commonly in data classification, but their disadvantages are low accuracy and unstability such as k-Means algorithm, c-Means algorithm, Iso-data algorithm. This paper proposes a method of combining fuzzy probability and fuzzy clustering algorithm to overcome these disadvantages. The method consists of two steps, first to calculate the number of clusters and the centroid of clusters based fuzzy probability, then to use fuzzy clustering algorithm to land-cover classification. The results showed that, the accuracy of the land cover classification using multispectral satellite images according to the developed method significantly increases compared with various algorithms such as k-Means, Iso-data.