Potential of Resolution-Enhanced Hyperspectral Data for Mineral Mapping Using Simulated EnMAP and Sentinel-2 Images

Spaceborne hyperspectral images are useful for large scale mineral mapping. Acquired at a ground sampling distance (GSD) of 30 m, the Environmental Mapping and Analysis Program (EnMAP) will be capable of putting many issues related to environment monitoring and resource exploration in perspective with measurements in the spectral range between 420 and 2450 nm. However, a higher spatial resolution is preferable for many applications. This paper investigates the potential of fusion-based resolution enhancement of hyperspectral data for mineral mapping. A pair of EnMAP and Sentinel-2 images is generated from a HyMap scene over a mining area. The simulation is based on well-established sensor end-to-end simulation tools. The EnMAP image is fused with Sentinel-2 10-m-GSD bands using a matrix factorization method to obtain resolution-enhanced EnMAP data at a 10 m GSD. Quality assessments of the enhanced data are conducted using quantitative measures and continuum removal and both show that high spectral and spatial fidelity are maintained. Finally, the results of spectral unmixing are compared with those expected from high-resolution hyperspectral data at a 10 m GSD. The comparison demonstrates high resemblance and shows the great potential of the resolution enhancement method for EnMAP type data in mineral mapping.

[1]  Stefania Matteoli,et al.  The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[2]  Naoto Yokoya,et al.  Hyperspectral Pansharpening: A Review , 2015, IEEE Geoscience and Remote Sensing Magazine.

[3]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[5]  Russell C. Hardie,et al.  Application of the stochastic mixing model to hyperspectral resolution enhancement , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Lei Zhang,et al.  Band-Subset-Based Clustering and Fusion for Hyperspectral Imagery Classification , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Jocelyn Chanussot,et al.  A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[8]  John Shepanski,et al.  Hyperion, a space-based imaging spectrometer , 2003, IEEE Trans. Geosci. Remote. Sens..

[9]  Stefan Kaiser,et al.  Simulation of Spatial Sensor Characteristics in the Context of the EnMAP Hyperspectral Mission , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Alexander F. H. Goetz,et al.  Terrestrial imaging spectrometry - Current status, future trends , 1993 .

[11]  Jocelyn Chanussot,et al.  Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource Data , 2014, IEEE Transactions on Image Processing.

[12]  F. Meer,et al.  Use of HyMap imaging spectrometer data to map mineralogy in the Rodalquilar caldera, southeast Spain , 2009 .

[13]  Menas Kafatos,et al.  Wavelet-based hyperspectral and multispectral image fusion , 2001, SPIE Defense + Commercial Sensing.

[14]  Jean-Yves Tourneret,et al.  Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Xiao Xiang Zhu,et al.  The J-SparseFI-HM Hyperspectral resolution enhancement method — Now fully automated , 2014, 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[16]  Harald van der Werff,et al.  Potential of ESA's Sentinel-2 for geological applications , 2014 .

[17]  Bin Wang,et al.  Fusion of Hyperspectral and Multispectral Images: A Novel Framework Based on Generalization of Pan-Sharpening Methods , 2014, IEEE Geoscience and Remote Sensing Letters.

[18]  G. Hunt SPECTRAL SIGNATURES OF PARTICULATE MINERALS IN THE VISIBLE AND NEAR INFRARED , 1977 .

[19]  Russell C. Hardie,et al.  MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor , 2004, IEEE Transactions on Image Processing.

[20]  Paul E. Johnson,et al.  Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site , 1986 .

[21]  Yifan Zhang,et al.  Noise-Resistant Wavelet-Based Bayesian Fusion of Multispectral and Hyperspectral Images , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Luciano Alparone,et al.  MTF-tailored Multiscale Fusion of High-resolution MS and Pan Imagery , 2006 .

[23]  David Krutz,et al.  DESIS (DLR Earth Sensing Imaging Spectrometer for the ISS-MUSES platform) , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[24]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[25]  Akira Iwasaki,et al.  Hyperspectral Imager Suite (HISUI) -Japanese hyper-multi spectral radiometer , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[26]  Mingyi He,et al.  Multi-spectral and hyperspectral image fusion using 3-D wavelet transform , 2007 .

[27]  José M. Bioucas-Dias,et al.  Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Bruno Aiazzi,et al.  Improving Component Substitution Pansharpening Through Multivariate Regression of MS $+$Pan Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[29]  S. J. Sutley,et al.  Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems , 2003 .

[30]  Fred A. Kruse,et al.  Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping , 2003, IEEE Trans. Geosci. Remote. Sens..

[31]  Luis Guanter,et al.  S2eteS: An End-to-End Modeling Tool for the Simulation of Sentinel-2 Image Products , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Patrick Hostert,et al.  The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation , 2015, Remote. Sens..

[33]  R. Singer Near-infrared spectral reflectance of mineral mixtures - Systematic combinations of pyroxenes, olivine, and iron oxides , 1981 .

[34]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[35]  Naoto Yokoya,et al.  Cross-Calibration for Data Fusion of EO-1/Hyperion and Terra/ASTER , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[36]  R. Green,et al.  NASA Mission to Measure Global Plant Physiology and Functional Types , 2008, 2008 IEEE Aerospace Conference.

[37]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[38]  M. Podwysocki,et al.  Geology, geochronology, fluid inclusions, and isotope geochemistry of the Rodalquilar gold alunite deposit, Spain , 1995 .

[39]  Ye Zhang,et al.  Integration of Spatial–Spectral Information for Resolution Enhancement in Hyperspectral Images , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Lucien Wald,et al.  Quality of high resolution synthesised images: Is there a simple criterion ? , 2000 .

[41]  Naoto Yokoya,et al.  Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Luis Guanter,et al.  Simulation of Optical Remote-Sensing Scenes With Application to the EnMAP Hyperspectral Mission , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Philippe Gamet,et al.  HYPXIM — A hyperspectral satellite defined for science, security and defence users , 2011, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[44]  Jianglin Ma,et al.  Superresolution reconstruction of hyperspectral remote sensing imagery using constrained optimization of POCS , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[45]  Jocelyn Chanussot,et al.  A Critical Comparison Among Pansharpening Algorithms , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Luis Guanter,et al.  EeteS—The EnMAP End-to-End Simulation Tool , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[47]  K. Moffett,et al.  Remote Sens , 2015 .

[48]  Tsehaie Woldai,et al.  Multi- and hyperspectral geologic remote sensing: A review , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[49]  Hervé Carfantan,et al.  Statistical Linear Destriping of Satellite-Based Pushbroom-Type Images , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[50]  R. Clark,et al.  Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications , 1984 .

[51]  Yasuyuki Matsushita,et al.  High-resolution hyperspectral imaging via matrix factorization , 2011, CVPR 2011.