An automatic endmember classification algorithm for hyperspectral data cubes. This algorithm is an improved version of a pattern recognition technology which was developed at Massachusetts Institute of Technology (MIT). The MIT algorithm is called the Extended Cross Correlation (XCC) technique, and it was designed to separate patterns from time resolved spectroscopic data. ASPIRE uses XCC as one of its core algorithms, but it features many improvements. These include: the use of Principle Components Analysis (PCA) to preprocess the data, and automatic endmember searching algorithm, and a Bayesian algorithm which is used to unmix the end-members. This invention also represents a new use of the XCC technology, because it had never before been used to identify spatial targets and patterns in hyperspectral data.