Anomaly detection for hyperspectral images using local tangent space alignment

Anomaly detection in hyperspectral images is investigated using local tangent space alignment (LTSA) for dimensionality reduction (DR) in conjunction with a minimum distance detector. The LTSA is implemented for large images by constructing a manifold with training data and employing the out-of-sample extension for testing data. The training data that should represent all the background types are generated by the recursive hierarchical segmentation (RHSEG) algorithm and the elimination of the very small segments that may represent anomalies. Experimental results indicate that the LTSA is able to distinguish anomalies from background using a small number of features in the embedded space, and the LTSA-based detector has superior anomaly detection performance to the well-known RX and kernel RX detectors.