Comparative analyses of unsupervised pca k-means change detection algorithm from the viewpoint of follow-up plan

HIGHLIGHTS

• who: Deniz Kenan Ku0131lu0131u00e7 and Peter Nielsen from the Department of Materials and Production, Aalborg University, Aalborg, Denmark have published the research work: Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan, in the Journal: Sensors 2022, 22, 9172. of /2022/
• what: In this study principal component analysis and 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 . . .
  

   

  

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