{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:04:52Z","timestamp":1760234692599,"version":"build-2065373602"},"reference-count":91,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T00:00:00Z","timestamp":1622160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003151","name":"Fonds de recherche du Qu\u00e9bec \u2013 Nature et technologies","doi-asserted-by":"publisher","award":["2014-MI-182452"],"award-info":[{"award-number":["2014-MI-182452"]}],"id":[{"id":"10.13039\/501100003151","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7\u201311.8 \u03bcm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL\/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).<\/jats:p>","DOI":"10.3390\/rs13112125","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T03:45:29Z","timestamp":1622432729000},"page":"2125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3121-4573","authenticated-orcid":false,"given":"Bardia","family":"Yousefi","sequence":"first","affiliation":[{"name":"Computer Vision and System Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Qu\u00e9bec, QC G2E 6J5, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0198-7439","authenticated-orcid":false,"given":"Clemente","family":"Ibarra-Castanedo","sequence":"additional","affiliation":[{"name":"Computer Vision and System Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Qu\u00e9bec, QC G2E 6J5, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Chamberland","sequence":"additional","affiliation":[{"name":"Telops Inc., 100-2600 St-Jean-Baptiste Ave, Qu\u00e9bec, QC G2E 6J5, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8777-2008","authenticated-orcid":false,"given":"Xavier P. V.","family":"Maldague","sequence":"additional","affiliation":[{"name":"Computer Vision and System Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Qu\u00e9bec, QC G2E 6J5, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4029-2648","authenticated-orcid":false,"given":"Georges","family":"Beaudoin","sequence":"additional","affiliation":[{"name":"Department of Geology and Geological Engineering, Laval University, Qu\u00e9bec, QC G2E 6J5, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,28]]},"reference":[{"key":"ref_1","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_2","unstructured":"(2021, May 01). Geotechnos. Available online: http:\/\/www.geotechnos.co.jp."},{"key":"ref_3","unstructured":"Yajima, T., Ohkawa, K., and Huzikawa, S. (2004, January 20\u201324). Hyperspectral alteration mineral mapping using the POSAM method. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2004), Anchorage, AK, USA."},{"key":"ref_4","first-page":"206","article-title":"Automatic Identification of Alteration Mineral Using a Portable Infrared Spectralmeter","volume":"21","author":"Huzikawa","year":"2001","journal-title":"J. Remote Sens. Soc. Jpn."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Davis, C.O. (2001). Airborne Hyperspectral Remote Sensing, Naval Research Lab. Technical Report.","DOI":"10.21236\/ADA625021"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1016\/j.rse.2007.12.014","article-title":"Three decades of hyperspectral remote sensing of the Earth: A personal view","volume":"113","author":"Goetz","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4955","DOI":"10.1109\/TGRS.2013.2286195","article-title":"Hyperspectral remote sensing image subpixel target detection based on supervised metric learning","volume":"52","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","first-page":"112","article-title":"Multi-and hyperspectral geologic remote sensing: A review","volume":"14","author":"Hecker","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","unstructured":"Boardman, J.W., Kruse, F.A., and Green, R.O. (1995). Mapping Target Signatures via Partial Unmixing of AVIRIS Data, Jet Propulsion Laboratory NASA."},{"key":"ref_10","first-page":"939","article-title":"Short wavelength infrared (SWIR) spectral analysis of hydrothermal alteration zones associated with base metal sulfide deposits at Rosebery and Western Tharsis, Tasmania, and Highway-Reward, Queensland","volume":"96","author":"Herrmann","year":"2001","journal-title":"Econ. Geol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Clark, R.N., Swayze, G.A., Livo, K.E., Kokaly, R.F., Sutley, S.J., Dalton, J.B., McDougal, R.R., and Gent, C.A. (2003). Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems. J. Geophys. Res. Planets, 108.","DOI":"10.1029\/2002JE001847"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1109\/LGRS.2011.2178814","article-title":"An enhanced physical method for downscaling thermal infrared radiance","volume":"9","author":"Liu","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014Interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/0034-4257(93)90024-R","article-title":"Expert system-based mineral mapping in northern Death Valley, California\/Nevada, using the airborne visible\/infrared imaging spectrometer (AVIRIS)","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Boardman, J.W. (1989). Inversion of Imaging Spectrometry Data Using Singular Value Decomposition, IEEE.","DOI":"10.1109\/IGARSS.1989.577779"},{"key":"ref_16","unstructured":"Boardman, J.W. (May, January 29). Sedimentary facies analysis using imaging spectrometry. Proceedings of the 8th Thematic Conference on Geologic Remote Sensing, Denver, CO, USA."},{"key":"ref_17","unstructured":"Center for the Study of Earth from Space (CSES) (1992). SIPS User\u2019s Guide, Spectral Image Processing System, Center for the Study of Earth from Space. Version 1.2."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/0034-4257(86)90044-1","article-title":"Color enhancement of highly correlated images. I. Decorrelation and HSI contrast stretches","volume":"20","author":"Gillespie","year":"1986","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/0034-4257(87)90088-5","article-title":"Color enhancement of highly correlated images. II. Channel ratio and \u201cchromaticity\u201d transformation techniques","volume":"22","author":"Gillespie","year":"1987","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"609","DOI":"10.2747\/1548-1603.49.4.609","article-title":"Estimation of light pollution using satellite remote sensing and geographic information system techniques","volume":"49","author":"Butt","year":"2012","journal-title":"GISci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/JSTARS.2010.2069085","article-title":"Urban image classification with semisupervised multiscale cluster kernels","volume":"4","author":"Tuia","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1109\/JSTARS.2011.2176721","article-title":"A quantitative and comparative assessment of unmixing-based feature extraction techniques for hyperspectral image classification","volume":"5","author":"Villa","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2209","DOI":"10.1109\/JSTARS.2013.2294053","article-title":"Informational clustering of hyperspectral data","volume":"7","author":"Pompilio","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5567","DOI":"10.1109\/TGRS.2013.2290372","article-title":"Semisupervised kernel feature extraction for remote sensing image analysis","volume":"52","author":"Bruzzone","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1109\/LGRS.2014.2320258","article-title":"A subspace-based multinomial logistic regression for hyperspectral image classification","volume":"11","author":"Khodadadzadeh","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","first-page":"377","article-title":"Regolith-geology mapping with support vector machine: A case study over weathered Ni-bearing peridotites, New Caledonia","volume":"64","author":"Sevin","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1109\/LGRS.2014.2302034","article-title":"A novel hierarchical semisupervised SVM for classification of hyperspectral images","volume":"11","author":"Shao","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/TGRS.2012.2201730","article-title":"Hyperspectral image classification via kernel sparse representation","volume":"51","author":"Chen","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","first-page":"986118","article-title":"Mineral identification in hyperspectral imaging using Sparse-PCA","volume":"Volume 9861","author":"Yousefi","year":"2016","journal-title":"Thermosense: Thermal Infrared Applications XXXVIII"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1109\/LGRS.2011.2158185","article-title":"Semisupervised band clustering for dimensionality reduction of hyperspectral imagery","volume":"8","author":"Su","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.isprsjprs.2016.09.001","article-title":"Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning","volume":"120","author":"Ma","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.oceaneng.2017.06.061","article-title":"Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory","volume":"142","author":"Chabane","year":"2017","journal-title":"Ocean Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6937","DOI":"10.1109\/TGRS.2014.2305805","article-title":"Active and semisupervised learning for the classification of remote sensing images","volume":"52","author":"Persello","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4032","DOI":"10.1109\/TGRS.2012.2228275","article-title":"Semisupervised self-learning for hyperspectral image classification","volume":"51","author":"Li","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6298","DOI":"10.1109\/TGRS.2013.2296031","article-title":"Spectral\u2013spatial classification of hyperspectral data using local and global probabilities for mixed pixel characterization","volume":"52","author":"Khodadadzadeh","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.1109\/36.934073","article-title":"Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery","volume":"39","author":"Funk","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1109\/TGRS.2005.861548","article-title":"An unsupervised artificial immune classifier for multi\/hyperspectral remote sensing imagery","volume":"44","author":"Zhong","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4175","DOI":"10.1109\/TGRS.2009.2023666","article-title":"Clustering of hyperspectral images based on multiobjective particle swarm optimization","volume":"47","author":"Paoli","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.neucom.2015.07.132","article-title":"Hierarchical feature learning with dropout k-means for hyperspectral image classification","volume":"187","author":"Zhang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1109\/LGRS.2008.2002319","article-title":"Unsupervised classification of hyperspectral-image data using fuzzy approaches that spatially exploit membership relations","volume":"5","author":"Bilgin","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1109\/TGRS.2012.2204759","article-title":"Classification and reconstruction from random projections for hyperspectral imagery","volume":"51","author":"Li","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2447","DOI":"10.1109\/JSTARS.2015.2398835","article-title":"A novel evolutionary swarm fuzzy clustering approach for hyperspectral imagery","volume":"8","author":"Ghamisi","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2940","DOI":"10.1109\/JSTARS.2017.2694439","article-title":"Hybrid Preprocessing Algorithm for Endmember Extraction Using Clustering, Over-Segmentation, and Local Entropy Criterion","volume":"10","author":"Kowkabi","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.isprsjprs.2014.08.006","article-title":"Automatic histogram-based fuzzy C-means clustering for remote sensing imagery","volume":"97","author":"Ghaffarian","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/JSTARS.2011.2162091","article-title":"Group and region based parallel compression method using signal subspace projection and band clustering for hyperspectral imagery","volume":"4","author":"Chang","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4162","DOI":"10.1109\/TGRS.2008.2001035","article-title":"Assessing the influence of reference spectra on synthetic SAM classification results","volume":"46","author":"Hecker","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1109\/36.934085","article-title":"Wavelets for computationally efficient hyperspectral derivative analysis","volume":"39","author":"Bruce","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2850","DOI":"10.1016\/j.rse.2008.01.016","article-title":"Continuous wavelets for the improved use of spectral libraries and hyperspectral data","volume":"112","author":"Rivard","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1002\/cjce.20343","article-title":"Bitumen content estimation of Athabasca oil sand from broad band infrared reflectance spectra","volume":"88","author":"Rivard","year":"2010","journal-title":"Can. J. Chem. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2012.12.018","article-title":"The longwave infrared (3\u201314 \u03bcm) spectral properties of rock encrusting lichens based on laboratory spectra and airborne SEBASS imagery","volume":"131","author":"Feng","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gupta, N. (2014). Development of spectropolarimetric imagers for imaging of desert soils. Proceedings of the 2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA, 14\u201316 October 2014, IEEE.","DOI":"10.1109\/AIPR.2014.7041908"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/TGRS.2002.1000319","article-title":"Cluster-space representation for hyperspectral data classification","volume":"40","author":"Jia","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1109\/TGRS.2002.800280","article-title":"Anomaly detection and classification for hyperspectral imagery","volume":"40","author":"Chang","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1109\/JSTARS.2012.2192472","article-title":"Spatial-spectral preprocessing prior to endmember identification and unmixing of remotely sensed hyperspectral data","volume":"5","author":"Martin","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"144","DOI":"10.2747\/1548-1603.42.2.144","article-title":"Spatial database development for Russian urban areas: A new conceptual framework","volume":"42","author":"Perepechko","year":"2005","journal-title":"GISci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1109\/JSTARS.2011.2173466","article-title":"A marker-based approach for the automated selection of a single segmentation from a hierarchical set of image segmentations","volume":"5","author":"Tarabalka","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/JSTARS.2012.2188278","article-title":"Practical evaluation of max-type detectors for hyperspectral images","volume":"5","author":"Bajorski","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"4248","DOI":"10.1109\/TGRS.2011.2169680","article-title":"Spatially adaptive hyperspectral unmixing","volume":"49","author":"Canham","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"229","DOI":"10.2747\/1548-1603.45.2.229","article-title":"Spatially explicit estimation of leaf area index using EO-1 Hyperion and Landsat ETM+ data: Implications of spectral bandwidth and shortwave infrared data on prediction accuracy in a tropical montane environment","volume":"45","author":"Twele","year":"2008","journal-title":"GISci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1109\/TGRS.2003.808879","article-title":"Principal-components-based display strategy for spectral imagery","volume":"41","author":"Tyo","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/LGRS.2012.2215005","article-title":"Hyperspectral imagery clustering with neighborhood constraints","volume":"10","author":"Li","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"755","DOI":"10.2747\/1548-1603.49.5.755","article-title":"Automatic pseudo-invariant feature extraction for the relative radiometric normalization of hyperion hyperspectral images","volume":"49","author":"Kim","year":"2012","journal-title":"GISci. Remote Sens."},{"key":"ref_64","first-page":"138","article-title":"Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis","volume":"32","author":"Padma","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2016.03.033","article-title":"Characterization of mineral substrates impregnated with crude oils using proximal infrared hyperspectral imaging","volume":"179","author":"Scafutto","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_66","first-page":"340","article-title":"Comparison of lithological mapping results from airborne hyperspectral VNIR-SWIR, LWIR and combined data","volume":"64","author":"Feng","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2016.04.008","article-title":"Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery","volume":"119","author":"Zhong","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"379","DOI":"10.2747\/1548-1603.47.3.379","article-title":"Applying an anomaly-detection algorithm for short-term land use and land cover change detection using time-series SAR images","volume":"47","author":"Qian","year":"2010","journal-title":"GISci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"130","DOI":"10.2747\/1548-1603.48.1.130","article-title":"Mixed-Pixel Decomposition of SAR Images Based on Single-Pixel ICA with Selective Members","volume":"48","author":"Yu","year":"2011","journal-title":"GISci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1109\/PROC.1976.10274","article-title":"An introduction to digitial matched filters","volume":"64","author":"Turin","year":"1976","journal-title":"Proc. IEEE"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Sofer, Y., Geva, E., and Rotman, S. (2009, January 9\u201311). Improved covariance matrices for point target detection in hyperspectral data. Proceedings of the 2009 IEEE International Conference on Microwaves, Communications, Antennas and Electronics Systems, Tel Aviv, Israel.","DOI":"10.1109\/COMCAS.2009.5385980"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/79.974724","article-title":"Detection algorithms for hyperspectral imaging applications","volume":"19","author":"Manolakis","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1109\/36.298007","article-title":"Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach","volume":"32","author":"Harsanyi","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_74","unstructured":"Broadwater, J., Meth, R., and Chellappa, R. (2004, January 20\u201324). A hybrid algorithm for subpixel detection in hyperspectral imagery. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2004), Anchorage, AK, USA."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1109\/36.934072","article-title":"Hyperspectral subpixel target detection using the linear mixing model","volume":"39","author":"Manolakis","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Joblove, G.H., and Greenberg, D. (1978). Color Spaces for Computer Graphics, ACM. ACM Siggraph Computer Graphics.","DOI":"10.1145\/800248.807362"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1002\/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G","article-title":"The application of cluster analysis in strategic management research: An analysis and critique","volume":"17","author":"Ketchen","year":"1996","journal-title":"Strateg. Manag. J."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Yousefi, B., Sojasi, S., Castanedo, C.I., Beaudoin, G., Huot, F., Maldague, X.P., Chamberland, M., and Lalonde, E. (2016). Emissivity retrieval from indoor hyperspectral imaging of mineral grains. SPIE Commercial+ Scientific Sensing and Imaging, International Society for Optics and Photonics.","DOI":"10.1117\/12.2224379"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"6219","DOI":"10.1364\/AO.57.006219","article-title":"Continuum removal for ground-based LWIR hyperspectral infrared imagery applying non-negative matrix factorization","volume":"57","author":"Yousefi","year":"2018","journal-title":"Appl. Opt."},{"key":"ref_80","unstructured":"(2017, May 01). Telops Inc. Available online: http:\/\/telops.com\/products\/hyperspectral-cameras\/item."},{"key":"ref_81","unstructured":"(2019, November 15). Isaac. Available online: https:\/\/github.com\/isaacgerg\/matlabHyperspectralToolbox."},{"key":"ref_82","unstructured":"McHugh, E.L., Girard, J.M., and Denes, L.J. (2003, January 28\u201330). Simplified hyperspectral imaging for improved geologic mapping of mine slopes. Proceedings of the Third International Conference on Intelligent Processing and Manufacturing of Materials, Vancouver, BC, Canada."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.2113\/econgeo.110.6.1375","article-title":"Characterizing Kimberlite Dilution by Crustal Rocks at the Snap Lake Diamond Mine (Northwest Territories, Canada) Using SWIR (1.90\u20132.36 \u03bcm) and LWIR (8.1\u201311.1 \u03bcm) Hyperspectral Imagery Collected from Drill Core","volume":"110","author":"Tappert","year":"2015","journal-title":"Econ. Geol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.infrared.2018.06.026","article-title":"Comparison assessment of low rank sparse-PCA based-clustering\/classification for automatic mineral identification in long wave infrared hyperspectral imagery","volume":"93","author":"Yousefi","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Inaba, M., Katoh, N., and Imai, H. (1994, January 6\u20138). Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering. Proceedings of the Tenth Annual Symposium on Computational Geometry, Stony Brook, NY, USA.","DOI":"10.1145\/177424.178042"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1145\/2027216.2027217","article-title":"Smoothed analysis of the k-means method","volume":"58","author":"Arthur","year":"2011","journal-title":"J. ACM (JACM)"},{"key":"ref_87","unstructured":"Traub, J.F. (1975). Multiple-Precision Zero-Finding Methods and the Complexity of Elementary Function Evaluation, Academic Press. Technical Report, Analytic Computational Complexity."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"2647","DOI":"10.1109\/JSTARS.2013.2272654","article-title":"Hyperspectral image visualization using band selection","volume":"7","author":"Su","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"4346","DOI":"10.1109\/TGRS.2018.2815588","article-title":"Decolorization-based hyperspectral image visualization","volume":"56","author":"Kang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1109\/TGRS.2008.2010129","article-title":"Interactive hyperspectral image visualization using convex optimization","volume":"47","author":"Cui","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"106409","DOI":"10.1016\/j.mineng.2020.106409","article-title":"Assessing the reliability of an automated system for mineral identification using LWIR Hyperspectral Infrared imagery","volume":"155","author":"Yousefi","year":"2020","journal-title":"Miner. Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2125\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:09:53Z","timestamp":1760162993000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2125"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,28]]},"references-count":91,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13112125"],"URL":"https:\/\/doi.org\/10.3390\/rs13112125","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,5,28]]}}}