Fast algorithm for exploring and compressing of large hyperspectral images

A new method for calculation of latent variable space for exploratory analysis and dimension reduction of large hyperspectral images is proposed. The method is based on significant downsampling of image pixels with preservation of pixels' structure in feature (variable) space. To achieve this, information about pixels density in principal component space for the first two components is utilized. The method was tested on several hyperspectral images and showed significant improvement of performance while the orientation of the latent variables was not very different from the original one. The method can be used first of all for fast compression of large data arrays with principal component analysis or similar projection techniques.