Data reduction techniques of hyperspectral images: A comparative study

In hyperspectral image analysis, the Principal Component Analysis (PCA) is most commonly used dimensionality reduction (DR) technique. This feature extraction method is known as PCA-DR. But PCA suffers from high computational cost and large memory requirement while applying to hyperspectral image. In this paper, the dimensionality reduction is carried out… (More)