Local-global background modeling for anomaly detection in hyperspectral images

In this paper, we address the problem of unsupervised detection of anomalies in hyperspectral images. Our proposed method is based on a novel statistical background modeling approach that combines local and global approaches. The local-global background model has the ability to adapt to all nuances of the background process like local approaches but avoids over-fitting due to a too high number of degrees of freedom, which produces a high false alarm rate. This is done by constraining the local background models to be interrelated. The results strongly prove the effectiveness of the proposed algorithm. We experimentally show that our local-global algorithm performs better than several other global or local anomaly detection techniques, such as the well known RX or its Gaussian Mixture version (GMRX).