A Modified Mixture Model-Based Clustering Algorithm for Resolving the Problem of Mixed Pixels Available in Satellite Imagery

In this study we present the model-based clustering in order to overcome the problem of mixed pixels for satellite imagery. The mixed pixel problem is one of the major reasons that affect the classification accuracy in the classification of remotely sensed images. Mixed pixels are usually the prime...

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Veröffentlicht in:Lobachevskii journal of mathematics Jg. 44; H. 11; S. 4824 - 4838
Hauptverfasser: Sherwani, A. R., Ali, Q. M., Ali, Irfan, Panta, Chom, Volodin, Andrei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Moscow Pleiades Publishing 01.11.2023
Schlagworte:
Algebra
Analysis
Geometry
Mathematical Logic and Foundations
Mathematics
Mathematics and Statistics
Probability Theory and Stochastic Processes
Bayesian information criteria
mixture model
Akaike information criteria
model-based clustering
integrated complete-data likelihood criteria
EM algorithm
ISSN:1995-0802, 1818-9962
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Zusammenfassung:In this study we present the model-based clustering in order to overcome the problem of mixed pixels for satellite imagery. The mixed pixel problem is one of the major reasons that affect the classification accuracy in the classification of remotely sensed images. Mixed pixels are usually the prime reason for degrading the success in image classification and object recognition. A modified model-based clustering algorithm is developed by modifying membership function and compared with the traditional model-based clustering algorithm in terms of classification error and brier score. Results on classification of satellite images reveal that the suggestive algorithms are robust and effective.
ISSN:1995-0802
1818-9962
DOI:10.1134/S199508022311029X