Unmixing of hyperspectral data for mineral detection using a hybrid method, Sar Chah-e Shur, Iran

This study aims to detect indicative minerals by spectral unmixing of the Hyperion and HyMap datasets in the Sar Chah-e Shur area. The mineral endmembers and their abundances were therefore determined using a series of hyperspectral processing algorithms. The virtual dimensionality methods including principal component analysis (PCA), minimum noise fraction (MNF), singular valued decomposition (SVD), Harsanyi-Farrand-Chang (HFC)/ (NWHFC), and Hyperspectral signal subspace identification by minimum error (HySime) were applied to estimate the number of endmembers. Five pure pixel-based methods including pixel purity index (PPI), sequential maximum angle convex cone (SMACC), simplex growing algorithm (SGA), N-FINDR, and vertex component analysis (VCA) were then applied for extracting the spectra of endmembers. Clay, serpentine, mica, and zeolite group minerals were identified which are consistent with the geological investigations in the region. The detected minerals were then mapped by the fully constrained least square (FCLS) method. The functionality of the methods and their performances on HyMap and Hyperion data were surveyed by several criteria including the number of recognized endmembers, the matching score of extracted endmembers with the reference spectrum, the agreement of the estimated abundances maps with the relevant lithological units on the geological map, and the average reconstruction error (ARE). Two hybrid maps were generated by combining individual methods that were found highly consistent with the geological map. The XRD analysis of three chips rock samples of two indicative lithological units was used to additionally check the efficiency of the applied methods.