Random-projection-based dimensionality reduction and decision fusion for hyperspectral target detection

Random projection for dimensionality reduction of hyperspectral imagery with a goal of target detection is investigated. Random projection is attractive in this task because it is data independent and computationally more efficient than other widely-used dimensionality-reduction methods, such as principal component analysis or the maximum-noise-fraction transform. Experimental results reveal that dimensionality reduction based on random projections yields improved target detection after decision fusion across multiple instances of the projections. Parallel implementation using a graphics processing unit is also investigated.