An MCD-based local ACE algorithm for hyperspectral imagery target detection

Unstructured detectors such as KGLRT, ACE and AMF are widely applied for target detection in hyperspectral imagery (HSI). However, conventional global and local approaches construct background model without considering the contamination caused by anomalies and suspected targets. This paper proposes a local ACE algorithm based on the minimum covariance determinant (MCD) estimator. In the proposed algorithm, a spectral angle based clustering method is applied to the whitened hyperspectral data to form several disjoint clusters over the whole image. Then for each cluster, the robust estimations of its background statistics are obtained using the MCD estimator. Finally, the ACE detector is applied to each pixel utilizing the robust background statistics of the cluster. With experimental results on two different real datasets, the superiority of the proposed algorithm is demonstrated.