A novel adaptive multilevel classification and detection method that takes into account both spectral and spatial characteristics of the data is proposed. Principal clusters are defined first, and those include background clusters and the predefined target clusters. The classification is done using minimum distance statistical classifier. Here, the main concern is to minimize misclassification rate, by allowing a number of pixels for which the classification confidence is low to remain unclassified at this level. The candidate clusters that are used in the analysis for the unclassified pixels are defined next. The candidate clusters are determined from both the spatial and spectral neighborhoods, using the labels of already classified pixels. Using defined candidate clusters, the mixing model analysis is performed. The linear least squares method to determine the fractions of particular candidate clusters in the corresponding pixel is applied. The results of the mixing model analysis are checked, and if the results of the analysis are satisfactory, the next step is performed. If the results of the analysis are not satisfactory, the candidate clusters list is renewed. After the loop processing has been completed for all pixels in the image, the target detection is performed. That process is based on comparing the estimated quantity of the pixels target endmember and the predefined thresholds. At the end, the detected targets are clustered, and their parameters are estimated. The proposed method was successfully applied to both synthetic and AVIRIS hyperspectral images of the Naval Air Station Fallon.