Method for selecting representative endmember components from spectral data

A process for determining at least one candidate spectral endmember that represents a group of N spectra comprising:

• building an initial endmember set composed of at least a first and a second spectrum representing at least a first and a second spectral characteristic expected to be in the group of N spectra;
• unmixing the N spectra within the group of spectra to determine what portion, if any, of each of the N spectra match at least one of the at least a first and a second spectrum;
• defining an error value for each of the N spectra, wherein the error value is the portion of each of the N spectra that does not match a combination of the at least a first and a second spectrum;
• comparing the error value for each of the N spectra to a predetermined error value range, wherein spectra having error values within the predetermined error value range are defined as M spectra;
• ordering the M spectra from highest error value to lowest error value;
• comparing each of the M spectra, beginning with the M spectra having the highest error value, to each of the N spectra, to determine the frequency with which each of the M spectra occurs within the N spectra;
• and
• calculating a metric for each of the M spectra, wherein the metric combines the frequency of occurrence of each of the M spectra within the N spectra with an error value for each of the M spectra, wherein the M spectra having the largest metric is the at least one candidate endmember.

A process for determining at least one candidate spectral endmember within a scene having N pixels using scene spectral data comprising:

• building a first endmember, wherein the first endmember represents a first spectral characteristic expected to be in the scene;
• building a second endmember for the scene, wherein the second endmember represents a second spectral characteristic expected to be in the scene;
• unmixing the N pixels in the scene to determine what portions of each of the N pixels match at least one of the first and second endmembers;
• defining a remaining portion for each of the N pixels not matching a combination of the first and second endmembers as an error value, wherein each error value corresponds to a residual spectra and the residual spectra in combination form a residual spectrum of the N pixels;
• calculating a root mean square (RMS) error for the N pixels by combining the error values for the residual spectra;
• determining an acceptable range of deviation from the mean RMS error;
• comparing each of the RMS error values for each of the N pixels to the acceptable range of deviation from the mean RMS error and keeping the M pixels that are within the acceptable range of deviation;
• ordering the M pixels from highest RMS error value to lowest RMS error value;
• comparing the corresponding residual spectra of the M pixels, beginning with the M pixel having the highest RMS error value, to the residual spectrum comprising the residual spectrum for the N pixels, to determine the frequency with which each of the corresponding residual spectra of the M pixels occurs within the residual spectra for the N pixels;
• and
• calculating a weighting factor for each of the M pixels, wherein the M pixel having the largest weighting factor is the at least one candidate endmember.