{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:22:38Z","timestamp":1760242958678,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2015,1,23]],"date-time":"2015-01-23T00:00:00Z","timestamp":1421971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pure surface materials denoted by endmembers play an important role in hyperspectral processing in various fields. Many endmember extraction algorithms (EEAs) have been proposed to find appropriate endmember sets. Most studies involving the automatic extraction of appropriate endmembers without a priori information have focused on N-FINDR. Although there are many different versions of N-FINDR algorithms, computational complexity issues still remain and these algorithms cannot consider the case where spectrally mixed materials are extracted as final endmembers. A sequential endmember extraction-based algorithm may be more effective when the number of endmembers to be extracted is unknown. In this study, we propose a simple but accurate method to automatically determine the optimal endmembers using such a method. The proposed method consists of three steps for determining the proper number of endmembers and for removing endmembers that are repeated or contain mixed signatures using the Root Mean Square Error (RMSE) images obtained from Iterative Error Analysis (IEA) and spectral discrimination measurements. A synthetic hyperpsectral image and two different airborne images such as Airborne Imaging Spectrometer for Application (AISA) and Compact Airborne Spectrographic Imager (CASI) data were tested using the proposed method, and our experimental results indicate that the final endmember set contained all of the distinct signatures without redundant endmembers and errors from mixed materials.<\/jats:p>","DOI":"10.3390\/s150202593","type":"journal-article","created":{"date-parts":[[2015,1,23]],"date-time":"2015-01-23T11:20:34Z","timestamp":1422012034000},"page":"2593-2613","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automatic Extraction of Optimal Endmembers from Airborne Hyperspectral Imagery Using Iterative Error Analysis (IEA) and Spectral Discrimination Measurements"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9190-2848","authenticated-orcid":false,"given":"Ahram","family":"Song","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anjin","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Earth and Environmental Sciences, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaewan","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 361-763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seokkeun","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 361-763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongil","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/TGRS.2003.819189","article-title":"Estimation of Number of Spectrally Distinct Signal Sources in Hypersepctral Imagery","volume":"42","author":"Chang","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1109\/LGRS.2011.2178815","article-title":"A New Sequential Algorithm for Hyperspectral Endmember Extraction","volume":"9","author":"Du","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/LGRS.2008.915934","article-title":"Hyperspectral Band Selection and Endmember Detection Using Sparsity Promoting Priors","volume":"5","author":"Zare","year":"2008","journal-title":"IEEE Int. Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"196","DOI":"10.3390\/s90100196","article-title":"Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines","volume":"9","author":"Plaza","year":"2009","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"33","DOI":"10.24102\/ijes.v3i1.442","article-title":"Advanced Remote Sensing Technology for Sustainable Land Development in Arid Lands","volume":"3","author":"Koch","year":"2014","journal-title":"Int. J. Environ. Sustain."},{"key":"ref_6","first-page":"4223","article-title":"A Discriminative Metric Learning Based Anomaly Detection Method","volume":"49","author":"Du","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/JSTARS.2013.2251863","article-title":"A Kernel-Based Target-Constrained Interference-Minimized Filter for Hypersepctral Sub-Pixel Target Detection","volume":"6","author":"Wang","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.patcog.2013.07.005","article-title":"Target Detection Based on a Dynamic Subspace","volume":"47","author":"Du","year":"2014","journal-title":"Pattern Recogn."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"768","DOI":"10.3390\/s90200768","article-title":"Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA\/CHRIS Data Over an Agricultural Area","volume":"9","author":"Sobrino","year":"2009","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4025","DOI":"10.1080\/01431161.2013.772313","article-title":"Using AHS Hyper-spectral Images to Study Forest Vegetation Recovery after a Fire","volume":"34","author":"Huesca","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Veganzones, M.A., Mura, M.D., Dumont, M., Zin, I., and Chanussot, J. (2014, January 13\u201318). Improved Subpixel Monitoring of Seasonal Snow Cover: A Case Study in the ALPS. Quebec city, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947356"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/s140100001","article-title":"Resolving Mixed Algal Species in Hyperspectral Images","volume":"14","author":"Mehrubeoglu","year":"2014","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1016\/j.rse.2008.11.007","article-title":"The ASTER Spectral Library Version 2.0","volume":"119","author":"Baldridge","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4223","DOI":"10.1109\/TGRS.2011.2162098","article-title":"A Hybrid Automatic Endmember Extraction Algorithm Based on a Local Window","volume":"49","author":"Li","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1109\/TGRS.2003.820314","article-title":"A Quantitative and Comparative Analysis of Endmember Extracion Algorithms from Hyperspectral Data","volume":"42","author":"Plaza","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","unstructured":"Boardman, J. (1993, January 25\u201329). Automating Spectral Unmixing of AVIRIS Data Using Convex Geometry Concepts. Arlington, MA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Neville, R.A., Stanenz, K., Szeredi, T., Lefebvre, J., and Hauff, P. (1999, January 21\u201324). Automatic Endmember Extraction from Hyperspectral Data for Mineral Exploration. Ottawa, ON, Canada.","DOI":"10.4095\/219526"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1117\/12.366289","article-title":"N-FINDR: An Algorithm for Fast Autonomous Spectral Endmember Determination in Hyperspecral Data","volume":"3753","author":"Winter","year":"1999","journal-title":"Proc. SPIE"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TGRS.2005.844293","article-title":"Vertex Component Analysis: A Fast Algorithm, to Unmix Hyperpsectral Data","volume":"43","author":"Nascimento","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.3390\/s8021321","article-title":"The Successive Projection Algorithm (SPA), an Algorithm with a Spatial Constraint for the Automatic Search of Endmembers in Hyperspectral Data","volume":"8","author":"Zhang","year":"2008","journal-title":"Sensors"},{"key":"ref_21","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":"Plaza","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1109\/TGRS.2006.879538","article-title":"Impact of Initialization on Design of Endmember Extraction Algorithms","volume":"44","author":"Plaza","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2804","DOI":"10.1109\/TGRS.2006.881803","article-title":"A New Growing Method for Simplex-Based Endmember Extraction Algorithm","volume":"44","author":"Chang","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1117\/12.602373","article-title":"An Improved N-FINDR Algorithm in Implementation","volume":"5806","author":"Plaza","year":"2005","journal-title":"Proc. SPIE"},{"key":"ref_25","unstructured":"Chang, C.I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic\/Plenum Publisher."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1109\/TGRS.2010.2057434","article-title":"Second Moment Linear Dimensionality as an Alternative to Virtual Dimensionality","volume":"49","author":"Bajorski","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1109\/TIP.2010.2071310","article-title":"Random N-FINDR (N-FINDR) Endmember Extraction Algorithms for Hyperspectral Imagery","volume":"20","author":"Chang","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chang, C.I. (2013). Finding Endmember for Hyperspectral Imagery. SPIE Newsroom.","DOI":"10.1117\/2.1201304.004798"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/JSTARS.2011.2119466","article-title":"Fast Algorithms to Implement N-FINDR for Hyperspectral Endmember Extraction","volume":"4","author":"Xiong","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","unstructured":"Atkinson, P.M., and Tate, N.J. (1999). Advances in Remote Sensing and GIS Analysis, Wiley."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/LGRS.2011.2172771","article-title":"A Low-Computational-Complexity Algorithm for Hyperspectral Endmember Extraction: Modified Vertex Component Analysis","volume":"9","author":"Lopez","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-based Approaches","volume":"5","author":"Plaza","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez, S., Paz, A., and Plaza, A. (2011, January 24\u201329). Real-Time Spectral Unmixing Using Iterative Error Analysis on Commodity Graphics Processing Units. Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049462"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/36.911111","article-title":"Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in Hyperspectral Imagery","volume":"39","author":"Heinz","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4112","DOI":"10.1109\/TGRS.2011.2155070","article-title":"Fully Constrained Least Squares Spectral Unmixing by Simplex Projection","volume":"49","author":"Heylen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","unstructured":"Chang, C.I. (July, January 28). Spectral Information Divergence for Hyperspectral Image Analysis. Hamburg, Germany."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1117\/1.1766301","article-title":"New Hyperspectral Discrimination Measure for Spectral Characterization","volume":"43","author":"Du","year":"2004","journal-title":"Opt. Eng."},{"key":"ref_38","first-page":"683","article-title":"Extended Tables of the Percentage Points of Student's t-distribution","volume":"54","author":"Federighi","year":"1959","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/TGRS.2011.2158829","article-title":"A New Maximum Simplex Volume Method Based on Householder Transformation for Endmember Extraction","volume":"50","author":"Liu","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1016\/j.rse.2011.02.013","article-title":"Supervised Vicarious Calibration (SVC) of Hyperspectral Remote-sensing Data","volume":"115","author":"Brook","year":"2011","journal-title":"Remote Sens. Environ"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/2\/2593\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:41:54Z","timestamp":1760215314000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/2\/2593"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,1,23]]},"references-count":40,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2015,2]]}},"alternative-id":["s150202593"],"URL":"https:\/\/doi.org\/10.3390\/s150202593","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2015,1,23]]}}}