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Remote Sens. 2016, 8(2), 127; doi:10.3390/rs8020127

EnGeoMAP 2.0—Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission

1
Helmholtz Center Potsdam, GFZ German Research Center for Geoscience, 14473 Potsdam, Germany
2
Institute for Earth and Environmental Science, University Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam-Golm, Germany
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Michael Rast, Véronique Carrere, Karl Staenz, Magaly Koch and Prasad S. Thenkabail
Received: 2 November 2015 / Revised: 25 January 2016 / Accepted: 1 February 2016 / Published: 5 February 2016

Abstract

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. View Full-Text
Keywords: EnMAP; Hyperion; EnGeoMAP 2.0; mineral mapping; imaging spectroscopy EnMAP; Hyperion; EnGeoMAP 2.0; mineral mapping; imaging spectroscopy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mielke, C.; Rogass, C.; Boesche, N.; Segl, K.; Altenberger, U. EnGeoMAP 2.0—Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission. Remote Sens. 2016, 8, 127.

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