  In recent years, hyperspectral anomaly detection (HAD) has become an active
topic and plays a significant role in military and civilian fields. As a
classic HAD method, the collaboration representation-based detector (CRD) has
attracted extensive attention and in-depth research. Despite the good
performance of CRD method, its computational cost is too high for the widely
demanded real-time applications. To alleviate this problem, a novel ensemble
and random collaborative representation-based detector (ERCRD) is proposed for
HAD. This approach comprises two main steps. Firstly, we propose a random
background modeling to replace the sliding dual window strategy used in the
original CRD method. Secondly, we can obtain multiple detection results through
multiple random background modeling, and these results are further refined to
final detection result through ensemble learning. Experiments on four real
hyperspectral datasets exhibit the accuracy and efficiency of this proposed
ERCRD method compared with ten state-of-the-art HAD methods.
