{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:59:43Z","timestamp":1760237983706,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,5]],"date-time":"2020-07-05T00:00:00Z","timestamp":1593907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005156","name":"Alexander von Humboldt Foundation","doi-asserted-by":"publisher","award":["Research Fellowship Fund"],"award-info":[{"award-number":["Research Fellowship Fund"]}],"id":[{"id":"10.13039\/100005156","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Geological objects are characterized by a high complexity inherent to a strong compositional variability at all scales and usually unclear class boundaries. Therefore, dedicated processing schemes are required for the analysis of such data for mineralogical mapping. On the other hand, the variety of optical sensing technology reveals different data attributes and therefore multi-sensor approaches are adapted to solve such complicated mapping problems. In this paper, we devise an adapted multi-optical sensor fusion (MOSFus) workflow which takes the geological characteristics into account. The proposed processing chain exhaustively covers all relevant stages, including data acquisition, preprocessing, feature fusion, and mineralogical mapping. The concept includes (i) a spatial feature extraction based on morphological profiles on RGB data with high spatial resolution, (ii) a specific noise reduction applied on the hyperspectral data that assumes mixed sparse and Gaussian contamination, and (iii) a subsequent dimensionality reduction using a sparse and smooth low rank analysis. The feature extraction approach allows one to fuse heterogeneous data at variable resolutions, scales, and spectral ranges and improve classification substantially. The last step of the approach, an SVM classifier, is robust to unbalanced and sparse training sets and is particularly efficient with complex imaging data. We evaluate the performance of the procedure with two different multi-optical sensor datasets. The results demonstrate the superiority of this dedicated approach over common strategies.<\/jats:p>","DOI":"10.3390\/s20133766","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T09:49:11Z","timestamp":1594028951000},"page":"3766","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multiple Optical Sensor Fusion for Mineral Mapping of Core Samples"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1091-9841","authenticated-orcid":false,"given":"Behnood","family":"Rasti","sequence":"first","affiliation":[{"name":"Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Exploration Division, 09599 Freiberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1203-741X","authenticated-orcid":false,"given":"Pedram","family":"Ghamisi","sequence":"additional","affiliation":[{"name":"Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Exploration Division, 09599 Freiberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4425-7655","authenticated-orcid":false,"given":"Peter","family":"Seidel","sequence":"additional","affiliation":[{"name":"Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Exploration Division, 09599 Freiberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8464-2331","authenticated-orcid":false,"given":"Sandra","family":"Lorenz","sequence":"additional","affiliation":[{"name":"Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Exploration Division, 09599 Freiberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4383-473X","authenticated-orcid":false,"given":"Richard","family":"Gloaguen","sequence":"additional","affiliation":[{"name":"Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Exploration Division, 09599 Freiberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","article-title":"Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geos. Remote Sens. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8830","DOI":"10.3390\/rs70708830","article-title":"The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation","volume":"7","author":"Guanter","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1146\/annurev-food-032818-121155","article-title":"Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications","volume":"10","author":"Ma","year":"2019","journal-title":"Annu. Rev. Food Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1080\/01431169608948728","article-title":"Identification and mapping of minerals in drill core using hyperspectral image analysis of infrared reflectance spectra","volume":"17","author":"Kruse","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"189","DOI":"10.29150\/jhrs.v7.4.p189-211","article-title":"The use of hyperspectral remote sensing for mineral exploration: A review","volume":"7","author":"Bedini","year":"2017","journal-title":"J. Hyperspectral Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JBO.19.9.096013","article-title":"Medical hyperspectral imaging: A review","volume":"19","author":"Lu","year":"2014","journal-title":"J. Biomed. Opt."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Weksler, S., Rozenstein, O., and Ben-Dor, E. (2018). Mapping Surface Quartz Content in Sand Dunes Covered by Biological Soil Crusts Using Airborne Hyperspectral Images in the Longwave Infrared Region. Minerals, 8.","DOI":"10.3390\/min8080318"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rost, E., Hecker, C., Schodlok, M., and van der Meer, F. (2018). Rock Sample Surface Preparation Influences Thermal Infrared Spectra. Minerals, 8.","DOI":"10.20944\/preprints201810.0376.v1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sture, \u00d8., Snook, B., and Ludvigsen, M. (2019). Obtaining Hyperspectral Signatures for Seafloor Massive Sulphide Exploration. Minerals, 9.","DOI":"10.3390\/min9110694"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1109\/TGRS.2003.812908","article-title":"Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping","volume":"41","author":"Kruse","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tusa, L., Andreani, L., Khodadadzadeh, M., Contreras, C., Ivascanu, P., Gloaguen, R., and Gutzmer, J. (2019). Mineral Mapping and Vein Detection in Hyperspectral Drill-Core Scans: Application to Porphyry-Type Mineralization. Minerals, 9.","DOI":"10.3390\/min9020122"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kirsch, M., Lorenz, S., Zimmermann, R., Tusa, L., M\u00f6ckel, R., H\u00f6dl, P., Booysen, R., Khodadadzadeh, M., and Gloaguen, R. (2018). Integration of Terrestrial and Drone-Borne Hyperspectral and Photogrammetric Sensing Methods for Exploration Mapping and Mining Monitoring. Remote Sens., 10.","DOI":"10.3390\/rs10091366"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jakob, S., Zimmermann, R., and Gloaguen, R. (2017). The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo\u2014A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data. Remote Sens., 9.","DOI":"10.3390\/rs9010088"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5160","DOI":"10.3390\/rs70505160","article-title":"Hyperspectral REE (Rare Earth Element) Mapping of Outcrops\u201a\u00c4\u00eeApplications for Neodymium Detection","volume":"7","author":"Boesche","year":"2015","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lorenz, S., Seidel, P., Ghamisi, P., Zimmermann, R., Tusa, L., Khodadadzadeh, M., Contreras, I.C., and Gloaguen, R. (2019). Multi-Sensor Spectral Imaging of Geological Samples: A Data Fusion Approach Using Spatio-Spectral Feature Extraction. Sensors, 19.","DOI":"10.3390\/s19122787"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/LGRS.2019.2924344","article-title":"Hyperspectral Mixed Gaussian and Sparse Noise Reduction","volume":"17","author":"Rasti","year":"2019","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rasti, B., Ghamisi, P., and Ulfarsson, M.O. (2019). Hyperspectral Feature Extraction Using Sparse and Smooth Low-Rank Analysis. Remote Sens., 11.","DOI":"10.3390\/rs11020121"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6976","DOI":"10.1109\/TGRS.2016.2593463","article-title":"Hyperspectral Feature Extraction Using Total Variation Component Analysis","volume":"54","author":"Rasti","year":"2016","journal-title":"IEEE Trans. Geos. Remote Sens."},{"key":"ref_21","unstructured":"Rasti, B., Hong, D., Hang, R., Ghamisi, P., Kang, X., Chanussot, J., and Benediktsson, J.A. (2020, March 05). Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox). Available online: https:\/\/arxiv.org\/abs\/2003.02822."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Scholkopf, B., and Smola, A.J. (2002). Learning with Kernels, MIT Press.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rasti, B., Scheunders, P., Ghamisi, P., Licciardi, G., and Chanussot, J. (2018). Noise Reduction in Hyperspectral Imagery: Overview and Application. Remote Sens., 10.","DOI":"10.3390\/rs10030482"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_25","first-page":"33","article-title":"Automated Scanning Electron Microscope Based Mineral Liberation Analysis An Introduction to JKMRC\/FEI Mineral Liberation Analyser","volume":"2","author":"Gu","year":"2003","journal-title":"J. Miner. Mater. Charact. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TGRS.2003.814625","article-title":"Classification and feature extraction for remote sensing images from urban areas based on morphological transformations","volume":"41","author":"Benediktsson","year":"2003","journal-title":"IEEE Trans. Geos. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/36.905239","article-title":"A New Approach for the Morphological Segmentation of High-resolution Satellite Imagery","volume":"39","author":"Pesaresi","year":"2001","journal-title":"IEEE Trans. Geos. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/LGRS.2017.2764059","article-title":"Automatic Hyperspectral Image Restoration Using Sparse and Low-Rank Modeling","volume":"14","author":"Rasti","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000016","article-title":"Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers","volume":"3","author":"Boyd","year":"2011","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear total variation based noise removal algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Physica D"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1057\/palgrave.jors.2600425","article-title":"Nonlinear Programming","volume":"48","author":"Bertsekas","year":"1997","journal-title":"J. Oper. Res. Soc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1023\/A:1017501703105","article-title":"Convergence of a block coordinate descent method for nondifferentiable minimization","volume":"109","author":"Tseng","year":"2001","journal-title":"J. Opt. Theory Appl."},{"key":"ref_33","unstructured":"Rasti, B. (2014). Sparse Hyperspectral Image Modeling and Restoration. [Ph.D. Thesis, Department of Electrical and Computer Engineering, University of Iceland]."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1109\/TGRS.2008.918089","article-title":"Hyperspectral Subspace Identification","volume":"46","author":"Nascimento","year":"2008","journal-title":"IEEE Trans. Geos. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Landgrebe, D.A. (2003). Signal Theory Methods in Multispectral Remote Sensing, John Wiley & Sons.","DOI":"10.1002\/0471723800"},{"key":"ref_36","unstructured":"Benediktsson, J.A., and Ghamisi, P. (2015). Spectral-Spatial Classification of Hyperspectral Remote Sensing Images, Artech House."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"106","DOI":"10.5589\/m09-018","article-title":"Mapping of hyperspectral AVIRIS data using machine learning algorithms","volume":"35","author":"Waske","year":"2009","journal-title":"Can. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1012450327387","article-title":"Choosing multiple parameters for support vector machines","volume":"46","author":"Chapelle","year":"2002","journal-title":"Mach. Learn."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/13\/3766\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:47:36Z","timestamp":1760176056000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/13\/3766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,5]]},"references-count":38,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20133766"],"URL":"https:\/\/doi.org\/10.3390\/s20133766","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,7,5]]}}}