Real-time hyperspectral anomaly detection system enhanced by graphics processing unit

Abstract

Hyperspectral images have the advantages of high spectral resolution and abundant spectral information, which helps researchers to analyze, process, and monitor land surface information. However, it also faces problems such as low degree of automatic data processing and poor timeliness of information extraction. This timeliness is due to the high dimensionality when extracting the target information in hyperspectral image. To solve this problem, a practical real-time hyperspectral anomaly detection system enhanced by graphics processing unit (GPU) is designed. The push-broom imaging spectrometers are adopted to obtain the image data line-by-line; then, the real-time anomaly detection algorithm based on QR decomposition is optimized using GPU (i.e., A QR decomposition is a decomposition of a matrix A into a product A = QR of an orthogonal matrix Q and an upper triangular matrix R). The results show that the parallel algorithm based on GPU is up to about 26 times faster than the serial algorithm based on central processing unit. This indicates that the proposed scheme significantly enhances the implementation efficiency while maintaining the accuracy of the results and can meet the application requirements of real-time anomaly detection well.