[IP1-6] Principal component analysis (PCA)

Principal component analysis (PCA) has proven to be of value in the analysis of multispectral and hyperspectral remotely sensed data. PCA is a technique that transforms the original correlated spectral dataset into a substantially smaller and easier set of uncorrelated variables that represents most of the information present in the original dataset. The first component accounts for the maximum proportion of the variance of the original dataset, and subsequent orthogonal components account for the maximum proportion of the remaining variance.

External resources

• Jensen, J. R. (2005). Introductory digital image processing : a remote sensing perspective (3rd ed.). Upper Saddle River, N.J.: Prentice Hall, p. 296.
• Metternicht, G. I., & Zinck, J. A. (2003). Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment, 85(1), 1-20. doi:https://doi.org/10.1016/S0034-4257(02)00188-8
• Zhao, G., & Maclean, A. L. (2000). A comparison of canonical discriminant analysis and principal component analysis for spectral transformation. Photogrammetric Engineering & Remote Sensing, 66(7), 841-847.

Learning outcomes

• 1136 - Convert multispectral image into its principal components

  Convert multispectral image into its principal components

Self assessment

Outgoing relations

• [IP1-6] Principal component analysis (PCA) is prerequisite of [IP1-7-2-1] Minimum noise fraction (MNF)
• [IP1-6] Principal component analysis (PCA) is prerequisite of [IP2-1-3-2] Principal Component Analysis (PCA)-based pan-sharpening
• [IP1-6] Principal component analysis (PCA) subclassof [IP1] Image pre-processing

Contributors

• Florian Albrecht
  https://www.researchgate.net/profile/Florian_Albrecht
• Mariana Belgiu
  https://research.utwente.nl/en/persons/mariana-belgiu