1. A method of determining a presence of a material from a hyperspectral image, the method comprising: receiving a first spectral signature corresponding to a region of interest (ROI) in the hyperspectral image; creating a model search space including two or more models, wherein each of the two or more models corresponds to a subset of spectral signatures in a library; creating, for each of the two or more models, a model spectral signature based on the corresponding subset of spectral signatures, wherein each of the model spectral signatures approximates the first spectral signature; calculating a cumulative probability of the first spectral signature indicating a presence of a material within the ROI, wherein the cumulative probability is based on a sum of similarity probabilities of models that contain the material as an element; and determining the presence of the material in the ROI based on the cumulative probability.
1. A method of determining a presence of a material from a hyperspectral image, the method comprising: receiving a first spectral signature corresponding to a region of interest (ROI) in the hyperspectral image; creating a model search space including two or more models, wherein each of the two or more models corresponds to a subset of spectral signatures in a library; creating, for each of the two or more models, a model spectral signature based on the corresponding subset of spectral signatures, wherein each of the model spectral signatures approximates the first spectral signature; calculating a cumulative probability of the first spectral signature indicating a presence of a material within the ROI, wherein the cumulative probability is based on a sum of similarity probabilities of models that contain the material as an element; and determining the presence of the material in the ROI based on the cumulative probability. 18. The method of claim 1 , further comprising determining an expected value of an error of a model in the model search space.