Hyperspectral anomaly detection (HAD) refers to a method of identifying abnormal targets through the differences in spectral separabilities of anomaly versus background clutter. It plays a significant role in fields such as commercial agriculture, for instance, in pest and disease monitoring and environmental monitoring. Collaborative representation detector (CRD) is a classic hyperspectral anomaly detection method. However, by constructing a sliding dual window, it leads to a high computational complexity and thus takes a relatively long time. In response to the deficiencies existing in that CRD method, we propose a method that first extracts extended morphological profiles (EMP) and then uses the obtained feature images to construct K-means CRD (EMPKCRD). This method performs window reconstruction on complex hyperspectral background pixels through the K-means clustering algorithm to separate abnormal pixels with similar features and obtain the background dictionary matrix. The method leverages the observation that background pixels can be effectively approximated by a linear combination of their spatially adjacent pixels, whereas anomalous pixels, due to their distinct nature, cannot be similarly reconstructed from their local neighborhood. This fundamental disparity in reconstructibility is then exploited to separate anomalies from the background. Then, anomaly detection can be carried out on this matrix faster, avoiding the high computational complexity caused by the use of a sliding dual window. Through comparative simulation experiments with seven widely used algorithms at present on three real-world datasets, the empirical evaluations validate that this method has excellent performance while exhibiting a favorable balance between detection precision and operational speed.