Multiple classifier system for remotely sensed data clustering
| Autor: | Mohammed El Amine Lazouni, Lamia Fatma Houbaba Chaouche Ramdane, Mostafa El Habib Daho, Habib Mahi |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Pattern recognition Multiple classifier QA76.75-76.765 ComputingMethodologies_PATTERNRECOGNITION Signal Processing Photography Computer software Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering TR1-1050 Cluster analysis business Software |
| Zdroj: | IET Image Processing, Vol 16, Iss 1, Pp 252-260 (2022) |
| ISSN: | 1751-9667 1751-9659 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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