GPU parallel implementation of improved noise adaptive principal component algorithm for feature extraction of hyperspectral images

The classification of Hyperspectral images (HSIs) has been the focus of many recent research efforts, where feature extraction plays an important role. Discriminative feature extraction methods aim to reduce the data dimension of HSIs, retain effective image information to the greatest extent, and suppress noises at the same time. Besides, according to the characteristics of pixel-by-pixel-multi-band of HSIs and data redundancy between bands, the processing of HSIs in the classifier will bring huge computational overhead. In this paper, we present a parallel implementation of the improved noise adaptive principal component algorithm (INAPC) for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). Aiming at maximizing the signal-to-noise ratio (SNR) instead of the variance, we firstly deploy two SVDs and more comprehensive noise estimation in the INAPC transform and constructed a complete feature extraction process. Then we deploy a complete CPU-GPU collaborative computing solution, and use several GPU programming optimization methods to achieve the maximum acceleration effect. Through the experiments on three real hyperspectral datasets, Experimental results show that the proposed INAPC has stable superiority and provides a significant speedup compared to the CPU implementation.