Anomaly detection (AD) is a particular case of target detection applications that aim to find the objects in the image scene which are anomalous in comparison to their surrounding background. In many applications, like real-time detection of environmental pollution, not only the accuracy of the algorithm, but also its runtime is important. Because AD algorithms run on high-dimensional data cubes, their runtimes are normally not optimal. This paper presents two high-speed AD algorithms: the first method is based on the maximum and minimum values of the spectral signature of hyperspectral data pixels; the other employs simple statistical and mathematical tools to calculate the abnormality of image pixels. The proposed methods have been compared with the Kernel-RX detector which is a well known, high performance AD. The AD methods have been applied to four hyperspectral datasets acquired by Airborne Visible/Infrared Imaging Spectrometer, Hyperspectral Mapper, and SpecTIR sensors. The evaluation of the algorithms has been done using receiver operation characteristic (ROC) curve, visual investigation, and runtime of the algorithms. Experimental results show that the detection performance of the proposed methods is almost equal to Kernel-RX detector. In addition, the proposed AD methods have a much better runtime than Kernel-RX detector and are fast enough to be implemented in real-time environmental applications.