Automatic identification of end-members for the spectral decomposition of remotely sensed scenes

Several methods have been proposed for the extraction of latent information from multispectral remotely sensed scenes based on the definition of indices and rotational transformations. A common drawback of these techniques is that they are ultimately based only on statistical relationships among pixel values rather than on physical characteristics of the scenes. Linear Pixel Unmixing is an alternative method which assumes that the pixel signal is the linear combination of some basic spectral components the fractions of which can be retrieved with good approximation. The method is straightforward and produces results which can be easily interpreted, but presents the problem of the identification of suitable end-members, which generally requires some external knowledge. In order to overcome this problem in the present research a statistical method is developed for the automatic identification of end-members. This methodology is composed by several steps, that are described and then applied to a case study with a Landsat 5 TM scene from Central Ethiopia (Africa). The results, evaluated in comparison with those of a more usual Principal Component transformation, indicate the good performance of the new procedure.