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
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-11, Vol.22 (23), p.9172
Main Authors: Kılıç, Deniz Kenan, Nielsen, Peter
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial satellites in remote sensing
Change detection
Cluster Analysis
Clustering
Comparative analysis
Computing time
Error analysis
Image filters
Methods
Neural networks
Parameter sensitivity
Planning
Principal Component Analysis
Principal components analysis
Radar
Remote sensing
SAR image change detection
SAR images
Synthetic aperture radar
unsupervised learning
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Summary: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 time, which are advantageous for scoring in the follow-up plan. Although there are many supervised methods described in the literature, unsupervised methods may be more appropriate in terms of computing time, data scarcity, and explainability in order to supply a trustworthy system. We consider the PCAKM algorithm, which is used as a benchmark method in many studies when making comparisons. Error metrics, computing times, and utility functions are calculated for 22 modified PCAKM regarding difference images and filtering methods. Various images with different characteristics affect the results of the configurations. However, it is evident that the PCAKM becomes less sensitive and more accurate for both the overall results and image results. Scoring by utilizing these results and other map information is a gap and innovation. Obtaining a change map in a fast, explainable, more robust and less sensitive way is one of the aims of our studies on scoring points in the follow-up plan.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22239172