One advantage of hyperspectral remote sensing is that it has more bands so more information could be used to recognize ground objects and estimate relative contents of materials. In this paper

pixels of hyperspectral remote sensing images of n bands are connected with points in an n-dimensional scatterplot. Pure pixels can be extracted using a method of simplex

which is a concept in convex geometry

and thus accurate hyperspectral image classification and spectral unmixing can be realized. The focus of this method is to find the simplex and to analyze it. The simplex can be found using MNF(minimum noise fraction) transform and PPI(pixel purity index) calculation

and the mapping methods used here are SAM(spectral angle mapper) classification and an unmixing method based on the simplex. All techniques here have been proved feasible by an application example. This paper also gives a procedure of the techniques. The advantages of the techniques and the procedure are that the endmenmbers for spectral mapping and unmixing can be extracted from the images themselves

and that spectral mapping and unmixing scale can be determined by users.