Fast Anomaly Detection Algorithms For Hyperspectral Images

Hyperspectral images have been used in anomaly and change detection applications such as search and rescue operations where it is critical to have fast detection. However, conventional Reed-Xiaoli (RX) algorithm [6] took about 600 seconds using a PC to finish the processing of an 800x1024 hyperspectral image with 10 bands. This is not acceptable for real-time applications. A more recent algorithm known as kernel RX (KRX) [7] achieves better detection performance than RX at the expense of computational cost. For example, for the same 800x1024 image with 10 bands, KRX took 15 hours to finish the processing. In this paper, we present a general framework for fast anomaly detection using RX and KRX algorithms. First, a fast data reduction scheme using Principal Component Analysis (PCA) is proposed. This method takes less than 1 second to finish and the performance degradation is minimal. Second, we propose several speed boosting options in the RX and KRX algorithms. These options include image sub-sampling, the use of block pixels, and background pixel sub-sampling. Actual hyperspectral image has been used in our studies. Receiver operating characteristics (ROC) curves and actual computation times were used to compare the various options. For the 800x1024x10 image, we were able to improve the speed by more than 220 times for RX and 700 times for KRX with minimal degradation in detection performance. Keywords—hyperspectral images; anomaly detection; RX; KRX; search and rescue.