Remote sensing has become an important source of urban land-use/cover classification, and as a result of their high spatial and spectral resolution, airborne hyperspectral images have been widely used to distinguish different urban classes. However, the previous studies into the classification of urban environments have mainly focused on a supervised scenario, which is limited by the selection of training samples. An unsupervised classification strategy utilizing an endmember extraction technique for airborne hyperspectral imagery is proposed by a combination of endmember extraction and k-means classification. The number of endmembers of the hyperspectral image is first estimated with the hyperspectral signal subspace identification with the minimum error method, and then the simplex growing algorithm is used to extract the endmember spectra that represent the different latent materials in the hyperspectral imagery. These latent materials were further integrated into the predefined number of classes and the k-means classification method was utilized to obtain the final classification map. Different distance measures were experimentally used in the procedure of class integration and classification to investigate the impact of initial cluster centers and further clustering criterion. The proposed strategy was compared with three traditional unsupervised classification methods, k-means, fuzzy k-means, and ISODATA, with two airborne hyperspectral images. The experimental results demonstrate that the proposed approach is more robust and outperforms the other three classification methods, and, hence, provides an innovative perspective for implementing the unsupervised classification of airborne hyperspectral imagery.