Anomaly detection with sparse unmixing and Gaussian mixture modeling of hyperspectral images

We propose an anomaly detection method that uses Gaussian mixture models for characterizing the scene background in hyperspectral images. First, the full spectrum is divided into several contiguous band groups for dimensionality reduction as well as for exploiting the peculiarities of different parts of the spectrum. Then, sparse spectral unmixing is performed for identifying significant endmembers in the scene, and hierarchical clustering in the abundance space is used for identifying pixel groups that contain these endmembers. Next, these pixel groups are used for initializing individual Gaussian mixture models that are estimated separately for each spectral band group. Finally, the Gaussian mixture models for all groups are fused for obtaining the final anomaly map for the scene. Comparative experiments showed that the proposed method performed better than two other density-based anomaly detectors, especially for small false positive rates, on an airborne hyperspectral data set.