In terms of small target detection in hyperspectral imagery,two classes of small target detection algorithms in hyperspectral imagery are studied,analyzed and compared,which are categorized into fully self-adaptive detectors and semi-supervised detectors.To fully self-adaptive detectors,calculations show that anomaly detector and whiteddistance abnormity anomaly detector (WAAD) are better than low probability target detector (LPTD) and uniform target detector (UTD).To semi-supervised detectors,elliptically contoured distributions detector with hyperbola threshold (ECDHyT) is the best judging from the receiver operating characteristic (ROC) curve.The reason is that ECDHyT is based on the elliptically contoured distributions which can characterize target and background influenced by many factors more precisely.Comparison between the two classes of the algorithing is made.Target detection efficiency can be improved remarkably with even a little prior information about the target.