Principal Component Analysis

Principal Component Analysis (PCA) is a technique used in data analysis to simplify complex datasets. It identifies the most important patterns, reducing the data’s dimensionality while preserving its key features. This helps in visualization and understanding of the data, making it easier to work with.

The model supports any imagery of arbitrary size. PCA in raster analysis has limitations. It assumes linear relationships, potentially missing complex non-linear patterns. It’s sensitive to outliers, which can distort results. Additionally, interpretability can be challenging due to transformed components. Finally, selecting the right number of components requires careful consideration, impacting the quality of dimensionality reduction