# GPU Parallel Implementation for Real-Time Feature Extraction of Hyperspectral Images  
[PERSON]  
1 State Key Laboratory of High-Performance Computing College of Computer, National University of Defense Technology, Changsha 410073, China; [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (M.S.) 1 State Key Laboratory of High-Performance Computing College of Computer, National University of Defense Technology, Changsha 410073, China; [EMAIL_ADDRESS] 1 State Key Laboratory of High-Performance Computing College of Computer, National University of Defense Technology  
However, according to the characteristics of pixel-by-pixel-multi-band of HSIs and data redundancy between bands, the processing of raw data in the classifier will bring huge computational overhead, which brings pressure to the real-time characteristics of image processing and affects the accuracy of classification, especially on mobile platforms, such as UAV. Thus, feature extraction (FE) is an important technique for HSIs data processing [17]. Many FE methods have been developed in recent years. Hyperspectral image feature extraction mainly includes linear FE methods and non-linear FE methods. Typical linear FE methods include principal components analysis (PCA) [18], linear discriminant analysis (LDA) [19], independent component analysis (ICA) [20], and maximum noise fraction (MNF) [21]. Non-linear FE methods are mainly based on manifold learning [22], graph theory, and kernel transformation. The linear FE methods are usually easy to implement and explain in practice, especially in some image classification tasks with extensive real-time search. On the other hand, high-performance computing (HPC) in hyperspectral image applications has also become a research focus for HSIs data processing [23; 24; 25]. However, the current parallel algorithms are mostly based on computer simulation.  
In this article, a flexible and efficient method is utilized in the noise adaptive principal component (NAPC) algorithm, specifically based on compute unified device architecture (CUDA). From noise estimation to feature extraction, we establish a complete CPU-GPU collaborative computing solution, which implements different CUDA parallelization key kernels and use some GPU programming optimization methods to achieve the maximum acceleration effect. Experiments show that the classification effect of the improved NAPC (INAPC) has stable superiority compared with other methods and provides a significant speedup compared with the OpenCV and PyTorch implementation. What's more, since most of the parallel algorithms are based on computer simulations, we conduct constructive and innovative flight experiments. We establish a complete set of UAV photoelectric platform, including UAV, hyperspectral camera, NVIDIA Jetson Xavier (AGX, Changsha, China), etc. Flight experimental results show, considering hyperspectral image data acquisition and transmission time, the proposed algorithm meets the feature extraction of real-time processing.  
The rest of this article is structured as follows. In Section 2, the INAPC algorithm is presented. Section 3 describes our parallel implementations and optimization methods in detail. Section 4 shows and discusses the results in terms of both computer simulation and flight experiments. The conclusions and future work are presented in Section 5.