  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 the CRD method, its computational cost mainly arising from the
sliding dual window strategy is too high for wide applications. Moreover, it
takes multiple repeated tests to determine the size of the dual window, which
needs to be reset once the dataset changes and cannot be identified in advance
with prior knowledge. To alleviate this problem, we proposed a novel ensemble
and random collaborative representation-based detector (ERCRD) for HAD, which
comprises two closely related stages. Firstly, we process the random
sub-sampling on CRD (RCRD) to gain several detection results instead of the
sliding dual window strategy, which significantly reduces the computational
complexity and makes it more feasible in practical applications. Secondly,
ensemble learning is employed to refine the multiple results of RCRD, which act
as various "experts" providing abundant complementary information to better
target different anomalies. Such two stages form an organic and theoretical
detector, which can not only improve the accuracy and stability of HAD methods
but also enhance its generalization ability. 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.
