Navas-Auger, William J.

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Spatial low-rank tensor factorization for hyperspectral image unmixing
(2021-05-14) Navas-Auger, William J.; Manian, Vidya; College of Engineering; Sierra, Heidy; Rivera Gallego, Wilson; Arzuaga Cruz, Emmanuel; Department of Computer Science and Engineering; Romero Oliveras, Juan R.
This work presents a method for hyperspectral image unmixing based on nonnegative tensor factorization. While traditional approaches may process spectral information without regard for spatial structures in the data set, tensor factorization preserves the spectral-spatial relationship which we intend to exploit. We used a rank-(L, L, 1) decomposition which approximates the original tensor as a sum of R components. Each component is a tensor resulting from the multiplication of a low-rank spatial representation and a spectral vector. Our approach uses the spatial factors, to identify high abundance areas where pure pixels (endmembers) may lie. Finally, abundance maps are generated by applying Fully Constrained Least Squares for each inferred endmember. Results of this method are compared against other approaches based on non-negative matrix and tensor factorization. We observed a significant reduction of spectral angle distance for extracted endmembers and equal or better root mean square error for abundance maps as compared with existing benchmarks.