A novel multi-level detection (MLD) method for detecting small targets within multispectral images, that takes into account both spectral and spatial characteristics of the data, is proposed. In the first level of processing, misclassification is minimized by applying minimum distance statistical classifier in conjunction with a spectral library of known class signatures. In a second level, the neighborhood of each unclassified pixel is analyzed for detection of candidate classes for use as endmembers in a spectral unmixing model. The fractions of neighborhood and target signatures for the unclassified pixels are determined by means of linear least-squares method. The third processing level determines the size and location of detected targets with a clustering analysis methodology. Target size and location are estimated on the basis of the sum and weighted vector mean, respectively, of the mixing fractions of the neighboring pixels. The MLD method was successfully applied to both synthetic and AVIRIS hyperspectral imagery data sets.