A modified mixture model-based clustering algorithm for resolving the problem of mixed pixels available in satellite imagery

DOI10.1134/s199508022311029xMaRDI QIDQ6544230

Andrei I. Volodin, A. R. Sherwani, Chom Panta, Qazi M. Ali, Irfan Ali

Publication date: 27 May 2024

Published in: Lobachevskii Journal of Mathematics (Search for Journal in Brave)

zbMATH Keywords

EM algorithmmodel-based clusteringmixture modelBayesian information criteriaAkaike information criteriaintegrated complete-data likelihood criteria

Mathematics Subject Classification ID

Statistics (62-XX) Parametric inference (62Fxx) Multivariate analysis (62Hxx)

Cites Work

• MCLUST: Software for model-based cluster analysis
• Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Model-Based Gaussian and Non-Gaussian Clustering
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Nearest-Neighbor Variance Estimation (NNVE)
• Finite mixture models
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: A modified mixture model-based clustering algorithm for resolving the problem of mixed pixels available in satellite imagery