APPLYING SEMI-SUPERVISED FUZZY C-MEANS CLUSTERING ALGORITHM BASED ON COLLABORATIVE CLUSTERING MODEL FOR LAND COVER CLASSIFICATION FROM LANDSAT-7 IMAGERY

Abstract

The rapid development of artificial satellites has led to an explosion of remote sensing data sources. Centralized storage of large data sources is becoming increasingly complex, and decentralized storage solutions on distributed systems are increasingly gaining attention. Traditional data mining techniques have become obsolete and are no longer suitable for solving large, multidimensional, distributed data problems. These datasets, for some reasons such as security, data transmission, privacy, etc., cannot be shared directly between computers but can only share information about cluster structure. This article presents a semi-supervised fuzzy c-means clustering algorithm based on the collaborative clustering model (CSFCM) on distributed systems applied to the problem of land cover classification from remote sensing data. The proposed model aims to solve the problem of land cover classification where remote sensing data is decentralized and stored on a distributed system of computers connected via the network. Experiments on four optical satellite image datasets show that the proposed method provides significantly better results in both classification quality and classification time compared to local clustering on individual datasets. This result suggests that developing collaborative model-based data analysis algorithms can help solve the problem of remote or distributed remote sensing image data analysis.