Multiple classifier system for remotely sensed data clustering

Abstract

Abstract The Multiple Classifier System (or classifier ensemble) is the consensus of different clustering algorithms that can provide high accuracy for the best partition and thus overcome the constraints of conventional approaches based on single classifiers. The MCS is divided into two stages: Partition creation and partition combining. The potential benefits of this methodology in unsupervised land cover categorization utilizing synthetic, composite, and remotely sensed data are investigated in this paper. Four clustering algorithms are used for the MCS's first step, and according to the WB index, the best‐unsupervised classification is obtained. In the second stage, relabeling and, voting approaches are then applied. The MCS's experimental results outperform the individual clustering outcomes in terms of accuracy.

Keywords

• Pattern recognition
• Data and information; acquisition, processing, storage and dissemination in geophysics
• Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research
• Optical, image and video signal processing
• Geophysical techniques and equipment