A Comparative Study of Classical Clustering Method and Cuckoo Search Approach for Satellite Image Clustering: Application to Water Body Extraction. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Labed, Kaouter; Fizazi, Hadria; Mahi, Habib; Galvan, Inés M.
• Is Part Of: Applied artificial intelligence. Volume 32:Issue 1(2018)
• Page Start: 96
• Page End: 118
• Publication Date: 2018-01-02
• Subjects: Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/08839514.2018.1451214
• Languages: English
• ISSNs: 0883-9514

Abstract

ABSTRACT: Image clustering is a critical and essential component of image analysis to several fields and could be considered as an optimization problem. Cuckoo Search (CS) algorithm is an optimization algorithm that simulates the aggressive reproduction strategy of some cuckoo species. In this paper, a combination of CS and classical algorithms (KM, FCM, and KHM) is proposed for unsupervised satellite image classification. Comparisons with classical algorithms and also with CS are performed using three cluster validity indices namely DB, XB, and WB on synthetic and real data sets. Experimental results confirm the effectiveness of the proposed approach.