Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan

Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan

In this study, principal component analysis and k-means clustering (PCAKM) methods for synthetic aperture radar (SAR) data are analyzed to reduce the sensitivity caused by changes in the parameters and input images of the algorithm, increase the accuracy, and make an improvement in the computation t...

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Bibliographic Details
Main Authors: Deniz Kenan Kılıç, Peter Nielsen
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Sensors
Subjects:
change detection
unsupervised learning
remote sensing
synthetic aperture radar
SAR image change detection
SAR images
Online Access:https://www.mdpi.com/1424-8220/22/23/9172

Internet

https://www.mdpi.com/1424-8220/22/23/9172

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