Automated Endmember Extraction Algorithm (AEEA)

Case Number: 2595TAMU08

TAMUS 2595 proposes a new method for automatically performing endmember extraction from hyperspectral data. This novel algorithm provides a fast and accurate calculated representation of high-dimensional data sets that may consist of multiple distributions. Once this representation is computed, the number of distributions can be determined without prior knowledge. For each distribution, an optimal transform can be determined that preserves informational content while reducing the data dimensionality, and hence, the computational cost. Finally, endmember extraction for the whole data set is accomplished. The inventors have performed tests using both synthetic and real hyperspectral images, and results indicate that the automated endmember extraction algorithm (AEEA) has the capability of autonomously determining endmembers from multiple clusters with computational speed and accuracy, while maintaining a robust tolerance to noise. Relative to a commonly used, standard hyperspectral endmember extraction algorithm, the algorithm has performed favorably or comparably in these initial tests. Another advantage is that since this new algorithm is not constrained to the number of bands +1 upper limit on endmembers, it is not only of utility for hyperspectral endmember-estimation, but it is also useful for multispectral applications, where the number of bands is limited.

Taxonomy: Software
Status: Marketing

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