Anomaly detection method based on improved minimum noise fraction transformation

In order to reduce the influence of noise on the detection results of hyperspectral anomaly detection and improve the rate of anomaly detection,a new anomaly detection process based on improved minimum noise fraction (MNF) transformation was proposed. Firstly, to improve the traditional MNF transform, the weighted neighborhood averaging method was used to estimate the noise matrix,a specific weight was given to each pixel of the neighbor matrix for increasing the portion of background pixels and suppressing the noise pixels in the sample matrix. It was an effective way to extract noise information by calculating the difference. Secondly, improved MNF transform was used to reduce the dimension of hyperspectral image data and to separate the noise from signals effectively.Finally, anomaly detection algorithm was implemented on low-dimensional denoised data. After actual test of AVIRIS data, the results show that the improved algorithm has better effect of reducing the dimension and separating the noise, and the rate of anomaly detection is improved significantly.