DOAJ Open Access 2022

Multiple classifier system for remotely sensed data clustering

Lamia Fatma Houbaba Chaouche Ramdane Habib Mahi Mostafa El Habib Daho Mohammed El Amine Lazouni

Abstrak

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.

Topik & Kata Kunci

Photography Computer software

Penulis (4)

Lamia Fatma Houbaba Chaouche Ramdane

Habib Mahi

Mostafa El Habib Daho

Mohammed El Amine Lazouni

Format Sitasi

APA MLA BibTeX

Ramdane, L.F.H.C., Mahi, H., Daho, M.E.H., Lazouni, M.E.A. (2022). Multiple classifier system for remotely sensed data clustering. https://doi.org/10.1049/ipr2.12349

Akses Cepat

PDF tidak tersedia langsung

Cek di sumber asli →

Lihat di Sumber doi.org/10.1049/ipr2.12349

Informasi Jurnal

Tahun Terbit: 2022
Sumber Database: DOAJ
DOI: 10.1049/ipr2.12349
Akses: Open Access ✓