In this paper, spectral image unmixing applied to Landsat Thematic mapper data from southern Spain is described to obtain a classified image based on abundance estimates of a number of spectral endmembers assuming linear mixing systematics. Spectral angle mapping (e.g. a technique by which a pixel spectrum is compared with a reference spectrum using the spectral angle between the two in a vector space) is used to distil the five most important endmembers out of a total of 12: (A) carbonate, (B) green vegetation, (C) dry vegetation, (D) hematite, and (E) kaolinite. The spectral unmixing final product is compared with classified images obtained using parallelepiped, maximumlikelihood, and k-nearest neighbour classification. This comparison demonstrates that high precision results can be obtained from spectral unmixing. Furthermore spectral unmixing overcomes some drawbacks of conventional classification methods: the root-mean square error and the total abundance image provide a means of assessing the accuracy of the analysis and spectral unmixing yields abundance estimates at a pixel support for all endmembers and thus allows a fuzzy-type of classification in which more then one class may be present at a pixel.