Unmixing of hyperspectral data for mineral detection using a hybrid method, Sar Chah-e Shur, Iran
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ORIGINAL PAPER
Unmixing of hyperspectral data for mineral detection using a hybrid method, Sar Chah-e Shur, Iran Hadi Jamshid Moghadam 1 & Majid Mohammady Oskouei 1 & Tohid Nouri 2 Received: 27 April 2020 / Accepted: 23 September 2020 # Saudi Society for Geosciences 2020
Abstract 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), NFINDR, 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. Keywords Hyperspectral data . Spectral unmixing . Endmember extraction . Virtual dimensionality . Pure pixel-based method . Abundance mapping
Introduction In recent years, remote sensing (RS) has been a leading technology for studying and monitoring the earth’s surface. It is attractive in various fields of studies such as geology, mining, environment, meteorology, agriculture, and hydrology. In this regard, a diversity of sensors with different capabilities and characteristics has been designed. In some cases, their spatial Responsible Editor: Biswajeet Pradhan * Majid Mohammady Oskouei [email protected] 1
Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran
2
Faculty of Engineering, University of Mohaghegh Ardabili, Ardabīl, Iran
and spectral resolution is not sometimes appropriate because of the limitations in the manufacturing of the sensor and it lowers the reliability and accuracy of the results. To deal with this issue, using the data with the higher spectral and spatial resolution is recommended (Bioucas-Dias et al. 2013; C
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