Linear spectral mixture analysis can be used to model the spectral var
iability In multi- or hyperspectral images and to relate the results t
o the physical abundance of surface constituents represented by the sp
ectral endmembers. The most difficult step in this analytical approach
lies in the selection of spectral endmembers, which are chosen to rep
resent surface components. A new approach to endmember selection is pr
esented here, which may be used to augment existing methods, in which
the endmembers are derived mathematically from the image data subject
to a set of user-defined constraints. The constraints take the form of
a starting model and allowable deviations from that starting model, w
hich incorporate anl a priori knowledge of the data and physical prope
rties of the scene. These constraints are applied to the basic mixing
equations, which are then solved iteratively to derive a set of spectr
al endmembers that minimize the residual error. Because the input to t
he model is quantitative, the derivation process is repeatable, and en
dmembers derived with different sets of constraints may be compared to
each other directly. Three examples are presented, in which spectral
endmembers are derived according to this model for a series of Images:
a synthetic image cube whose endmembers are already known, a natural
terrestrial scene, and a natural lunar scene. Detailed analysis of the
model inputs and results reveal that this modified approach to endmem
ber selection provides physically realistic spectral endmembers that i
n many cases represent purer components than could be found in arty pi
xel in the image scene. (C)Elsevier Science Inc., 1997.