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Christian Mielke, et al., report that algorithms for a rapid analysis of hyperspectral data are becoming more and more important with planned next generation spaceborne hyperspectral missions such as the Environmental Mapping and Analysis Program (EnMAP) and the Japanese Hyperspectral Imager Suite (HISUI), together with an ever growing pool of hyperspectral airborne data. The here presented EnGeoMAP 2.0 algorithm is an automated system for material characterization from imaging spectroscopy data, which builds on the theoretical framework of the Tetracorder and MICA (Material Identification and Characterization Algorithm) of the United States Geological Survey and of EnGeoMAP 1.0 from 2013.


EnGeoMAP 2.0 includes automated absorption feature extraction, spatio-spectral gradient calculation and mineral anomaly detection. The usage of EnGeoMAP 2.0 is demonstrated at the mineral deposit sites of Rodalquilar (SE-Spain) and Haib River (S-Namibia) using HyMAP and simulated EnMAP data. Results from Hyperion data are presented as supplementary information. 


Their paper shows that EnGeoMAP 2.0 adds four aspects to the automated analysis of imaging spectroscopy data with expert systems.It is able to produce material maps using the geometric hull automated absorption feature definition, which requires no a priori knowledge about the shape of the reference library spectra or the image spectra. This is different to the fixed defined features of part of the USGS Tetracorder or MICA.

The EnGeoMAP 2.0 algorithm is able to incorporate sensor specific SNR information. Otherwise user defined minimum absorption depths for VNIR and SWIR absorption features may be used. The calculation of spatio-spectral gradients of e.g., EnMAP data is possible by hyperspectral gradient detection. It may be used to outline areas of high spatial material heterogeneity. These areas may otherwise only be resolved by calculating material maps from cost intensive hyperspectral airborne image scenes.
EnGeoMAP 2.0 includes the automatic detection of mineral anomalies (e.g., gossans or hydrothermal alteration zones), which is not part of the USGS Tetracorder or MICA in the form described here.

EnGeoMAP 2.0 widens the user base of hyperspectral data and data products to more end-users. This enables exploration geologists and consultants in geoscience to facilitate these ready-to-use mineral maps and exploration anomaly maps without an explicit in-depth knowledge of imaging spectroscopy. Simulated EnMAP data has shown the great potential of the EnMAP mission to characterize mineral deposit sites and to highlight exploration anomalies via space-borne hyperspectral data using the here presented EnGeoMAP 2.0 algorithm. EnMAP significantly outperforms the currently available Hyperion sensor in characterizing the surface mineralogy at the mineral deposit sites of Rodalquilar and Haib River.

Although Hyperion shows the least potential for mineral mapping it still is a very useful spaceborne sensor for automated mineral mapping using, as it is to date the only available spaceborne imaging spectrometer covering also the SWIR spectral range. Future versions of EnGeoMAP may be extended by an incorporation of thermal infrared data into the analysis process together with more mineral deposit models for exploration anomaly characterization.


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