Abstract
The paper presents histogram-based initialzation of Fuzzy C Means (FCM) clustering algorithm for remote sensing image analysis. The drawback of well known FCM clustering is sensitive to the choice of initial cluster centers. In order to overcome this drawback, the proposed algorithm, first, determines the optimal initial cluster centers by maximizing the histogram-based weight function. By using these initial cluster centers, the given image is segmented using fuzzy clustering. The major contribution of the proposed method is the automatic initialization of the cluster centers and hence, the clustering performance is enhanced. Also, it is empirically free of experimentally set parameters. Experiments are performed on remote sensing images and cluster validity indices Davies-Bouldin, Partition index, Xie-Beni, Partition Coefficient and Partition Entropy are computed and compared with prominent methods such as FCM, K-Means, and automatic histogram based FCM. The experimental outcomes show that the proposed method is competent for remote sensing image segmentation.
Highlights
Experiments are performed on remote sensing images and cluster validity indices Davies-Bouldin, Partition index, Xie-Beni, Partition Coefficient and Partition Entropy are computed and compared with prominent methods such as Fuzzy C Means (FCM), K-Means, and automatic histogram based FCM
The experimental outcomes show that the proposed method is competent for remote sensing image segmentation
We focus on the clustering methods using the minimization of an objective function, which can be further divided into two main clustering strategies: Keywords
Summary
Remote sensing images are used to extract land cover information which is useful for many applications based on Geographical Information System (GIS), such as creation & update of maps, infrastructure development, disaster planning, and military operations. The hard clustering methods classify each data point or pixel to one of the clusters, the results are often very crispy. This crisp clustering causes some difficulties in remote sensing images, which have limited spatial resolution, poor contrast, the complexity of the ground surface and diversity of disturbance or a spectral variation [7]. Soft clustering methods are based on Fuzzy set theory [8] and [9] and invoke the concept of partial membership function, which has been widely used in data clustering and image segmentation. The FCM [11] is one of the most popular and successful algorithms used for image c 2020 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING segmentation because it has robust characteristics for cluster validation indexes and results obtained from the ambiguity and can retain much more information than algorithm and Sec. 4. concludes the paper. hard segmentation methods [12]
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