Rapid detection of total nitrogen content in soil based on hyperspectral technology

The Kosedag region, is located in Central-Eastern Anatolia, contains a lot of base metals (Pb–Zn, Cu) and Au occurrences. The region is explored by numerous mining companies and MTA. In this research, hydrothermal alteration mapping by hyperspectral Hyperion satellite data was carried out to contribute to these explorations in a part of the region. Hydrothermal alteration is one of the initial steps in the exploration of such metallic occurrences. This study area was chosen to test the accuracy of the hyperspectral data results with those of field and laboratory study results. The in situ alteration map was prepared during field surveys and numerous samples analyses. These samples were investigated by microscopy and XRD examinations. The minerals which are belonging to argillic, sericitic, propylitic, and FeOx (iron oxide) hydrothermal alterations were determined. These minerals were checked and confirmed on the surfaces of altered samples by using ASD fieldspec 4 hi-res. The mixture tuned matched filtering (MTMF), which is one of the widely used spectral classification methods, was applied on the Hyperion data to determine the distributions of these alteration minerals in the study area. The results have shown that the comparison of field and laboratory studies and MTMF results coincided with an overall accuracy of over 86% and a kappa coefficient of 0.80. The alteration map has been generated using the MTMF method, has been the first hyperspectral research of the hydrothermal alteration mineralogy in the region. Consequently, the generated map can be used as a basic alteration map during exploration studies of hydrothermal base metal mineralizations in the region.

Hydrothermal alteration mapping using EO-1 Hyperion hyperspectral data in Kösedağ, Central-Eastern Anatolia (Sivas-Turkey)

We applied the Modified Spectral Angle Mapper MSAM and continuum-removal methods to reflectance data obtained in the shortwave infrared regions by the airborne hyperspectral sensor, HyMap, to delin...

Mineralogical mapping of southern Namibia by application of continuum-removal MSAM method to the HyMap data

The pixels of remote images often contain more than one distinct material (mixed pixels), and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared effort has enabled the development of techniques for retrieving information from mixed pixels. The most analyzed, implemented, and employed procedure is spectral unmixing. Among the extensive literature on the spectral unmixing, nineteen reviews were identified, and each highlighted the many shortcomings of spatial validation. Although an overview of the approaches used to spatially validate could be very helpful in overcoming its shortcomings, a review of them was never provided. Therefore, this systematic review provides an updated overview of the approaches used, analyzing the papers that were published in 2022, 2021, and 2020, and a dated overview, analyzing the papers that were published not only in 2011 and 2010, but also in 1996 and 1995. The key criterion is that the results of the spectral unmixing were spatially validated. The Web of Science and Scopus databases were searched, using all the names that were assigned to spectral unmixing as keywords. A total of 454 eligible papers were included in this systematic review. Their analysis revealed that six key issues in spatial validation were considered and differently addressed: the number of validated endmembers; sample sizes and sampling designs of the reference data; sources of the reference data; the creation of reference fractional abundance maps; the validation of the reference data with other reference data; the minimization and evaluation of the errors in co-localization and spatial resampling. Since addressing these key issues enabled the authors to overcome some of the shortcomings of spatial validation, it is recommended that all these key issues be addressed together. However, few authors addressed all the key issues together, and many authors did not specify the spatial validation approach used or did not adequately explain the methods employed. 

https://www.mdpi.com/2072-4292/15/11/2822/pdf?version=1685543544

Spatial Validation of Spectral Unmixing Results: A Systematic Review

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.

Hadi Jamshid Moghadam

Papers

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

Improved minimum volume simplex analysis algorithm for unmixing of hyperspectral data