tailieunhanh - Combining fuzzy probability and fuzzy clustering for multispectral satellite imagery classification
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. | Tạp chí Khoa học và Công nghệ 54 (3) (2016) 300-313 DOI: COMBINING FUZZY PROBABILITY AND FUZZY CLUSTERING FOR MULTISPECTRAL SATELLITE IMAGERY CLASSIFICATION Dinh-Sinh Mai*, Le-Hung Trinh, Long Thanh Ngo Le Quy Don Technical University, Hoang Quoc Viet Road, Bac Tu Liem , Hanoi * Email: maidinhsinh@ Received: 23 June 2015; Accepted for publication: 2 March 2016 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. Keyword: satellite imagery, probability, fuzzy c-means clustering. 1. INTRODUCTION The algorithms applied to image segmentation such as k-Means, c-Means, Iso-data show the same way based on the euclidean distance to determine the degree of similarity between the considered objects and cluster centroids. In problems of land cover classification, methods based on statistical parameters have been widely used because they are easy to implement and highly accurate [1 - 3]. However, these methods are quite expensive, time consuming and unsuitable. Fuzzy logic has been widely applied in most of scientific and technical fields [4 -
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