{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T14:52:35Z","timestamp":1784904755091,"version":"3.55.0"},"reference-count":55,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T00:00:00Z","timestamp":1599782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Oceans and Fisheries, Korea Research Institute of Ships and Ocean engineering","award":["PES3650"],"award-info":[{"award-number":["PES3650"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As marine transportation has increased in coastal regions, maritime accidents associated with vessels have steadily increased. Remotely sensed satellite or airborne images can aid rapid vessel monitoring over wide areas at high resolutions. In this study, airborne hyperspectral experiments were performed to detect marine vessels mainly including fishing boat and yacht by applying pixel-based mixture techniques and to estimate the size of the vessels through an objective ellipse fitting method. Various spectral libraries of marine objects and seawaters were constructed through in-situ experiments for spectral analysis of the internal structures of vessels. The hyperspectral images were dimensionally reduced through principal component analysis. Several hyperspectral mixture algorithms, such as N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA), were used for the detection of vessels. The N-FINDR and VCA techniques presented a total of 14 vessels, the ICA technique detected seven vessels, and the PPI technique detected two vessels. The pixel-based probability of detection (POD) and false alarm ratio (FAR) for all 14 vessels were 96.40% and 4.30%, respectively. The sizes of the vessels were estimated by extracting the boundaries of the vessels through a two-dimensional gradient and applying the ellipse fitting method. Compared with the digital mapping camera (DMC) images with resolutions of 0.10 m, the root-mean-square errors of the length and width of the vessels were approximately 1.19 m and 0.81 m, respectively. The application of spectral mixing methods provided a high probability of detecting the objects, as well as the overall structures of the decks of the vessels.<\/jats:p>","DOI":"10.3390\/rs12182968","type":"journal-article","created":{"date-parts":[[2020,9,13]],"date-time":"2020-09-13T21:11:32Z","timestamp":1600031492000},"page":"2968","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Application of Spectral Mixture Analysis to Vessel Monitoring Using Airborne Hyperspectral Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Jae-Jin","family":"Park","sequence":"first","affiliation":[{"name":"Department of Science Education, Seoul National University, Seoul 08826, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tae-Sung","family":"Kim","sequence":"additional","affiliation":[{"name":"Maritime Safety and Environmental Research Division, Korea Research Institute of Ships and Ocean Engineering, Daejeon 38103, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8899-7201","authenticated-orcid":false,"given":"Kyung-Ae","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Earth Science Education\/Research Institute of Oceanography, Seoul National University, Seoul 08826, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1545-0253","authenticated-orcid":false,"given":"Sangwoo","family":"Oh","sequence":"additional","affiliation":[{"name":"Maritime Safety and Environmental Research Division, Korea Research Institute of Ships and Ocean Engineering, Daejeon 38103, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4665-3333","authenticated-orcid":false,"given":"Moonjin","family":"Lee","sequence":"additional","affiliation":[{"name":"Maritime Safety and Environmental Research Division, Korea Research Institute of Ships and Ocean Engineering, Daejeon 38103, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0435-4573","authenticated-orcid":false,"given":"Pierre-Yves","family":"Foucher","sequence":"additional","affiliation":[{"name":"Theoretical and Applied Optics Department, Office National d\u2019Etudes et Recherches A\u00e9rospatiales, F-31055 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1126\/science.278.5341.1209c","article-title":"Estimates of coastal populations","volume":"278","author":"Cohen","year":"1997","journal-title":"Science"},{"key":"ref_2","first-page":"584","article-title":"A global analysis of human settlement in coastal zones","volume":"19","author":"Small","year":"2003","journal-title":"J. Cosatal Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.ecolecon.2006.10.022","article-title":"The coasts of our world: Ecological, economic and social importance","volume":"63","author":"Intralawan","year":"2007","journal-title":"Ecol. Econ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.12.033","article-title":"Vessel detection and classification from spaceborne optical images: A literature survey","volume":"207","author":"Kanjir","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_5","first-page":"291","article-title":"Ship detection from LANDSAT imagery","volume":"44","author":"McDonnell","year":"1978","journal-title":"Photogram Eng. Rem. Sens."},{"key":"ref_6","unstructured":"Halvorson, C.S., Lehrfeld, D., and Saito, T.T. (2008). Enhanced ship detection from overhead imagery. Optics and Photonics in Global Homeland Security IV, SPIE."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/LGRS.2009.2031826","article-title":"Characterization of a Bayesian ship detection method in optical satellite images","volume":"7","author":"Proia","year":"2009","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_8","unstructured":"Johansson, P. (2011). Small Vessel Detection in High Quality Optical Satellite Imagery, Chalmers University of Technology."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1080\/07038992.1997.10874677","article-title":"Ship detection by the RADARSAT SAR: Validation of detection model predictions","volume":"23","author":"Vachon","year":"1997","journal-title":"Can. J. Remote Sens."},{"key":"ref_10","unstructured":"Tello, M., Lopez-Martinez, C., Mallorqui, J., and Bonastre, R. (August, January 31). Automatic detection of spots and extraction of frontiers in SAR images by means of the wavelet transform: Application to ship and coastline detection. Proceedings of the IEEE IGARSS, Denver, CO, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3426","DOI":"10.1109\/TGRS.2007.907192","article-title":"A new application for PolSAR imagery in the field of moving target indication\/ship detection","volume":"45","author":"Liu","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1109\/TGRS.2010.2071879","article-title":"Ship surveillance with TerraSAR-X","volume":"49","author":"Brusch","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/TGRS.2016.2606481","article-title":"New hierarchical saliency filtering for fast ship detection in high-resolution SAR images","volume":"55","author":"Wang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","first-page":"2819","article-title":"Ship detection in polarimetric sar images via variational bayesian inference","volume":"10","author":"Song","year":"2017","journal-title":"IEEE J-STARS"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1080\/07038992.2001.10854896","article-title":"Automatic detection of ships in RADARSAT-1 SAR imagery","volume":"27","author":"Wackerman","year":"2001","journal-title":"Can. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1109\/LGRS.2008.915593","article-title":"Using SAR images to detect ships from sea clutter","volume":"5","author":"Liao","year":"2008","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_17","first-page":"4585","article-title":"An improved iterative censoring scheme for CFAR ship detection with SAR imagery","volume":"52","author":"An","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Brando, V.E., Lovell, J.L., King, E.A., Boadle, D., Scott, R., and Schroeder, T. (2016). The potential of autonomous ship-borne hyperspectral radiometers for the validation of ocean color radiometry data. Remote Sens., 8.","DOI":"10.3390\/rs8020150"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rostami, M., Kolouri, S., Eaton, E., and Kim, K. (2019). Deep transfer learning for few-shot sar image classification. Remote Sens., 11.","DOI":"10.20944\/preprints201905.0030.v1"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gao, F., Shi, W., Wang, J., Yang, E., and Zhou, H. (2019). Enhanced feature extraction for ship detection from multi-resolution and multi-scene synthetic aperture radar (SAR) images. Remote Sens., 11.","DOI":"10.3390\/rs11222694"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, S., Zhang, J., and Zhan, R. (2020). R2FA-Det: Delving into high-quality rotatable boxes for ship detection in SAR images. Remote Sens., 12.","DOI":"10.3390\/rs12122031"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Song, J., Kim, D.J., and Kang, K.M. (2020). automated procurement of training data for machine learning algorithm on ship detection using ais information. Remote Sens., 12.","DOI":"10.3390\/rs12091443"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1672\/18-20","article-title":"Hyperspectral image data for mapping wetland vegetation","volume":"23","author":"Hirano","year":"2003","journal-title":"Wetlands"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rse.2006.11.002","article-title":"Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor","volume":"108","author":"Pengra","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1016\/j.jenvman.2007.06.028","article-title":"Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing","volume":"90","author":"Zomer","year":"2009","journal-title":"J. Environ. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8494","DOI":"10.3390\/rs6098494","article-title":"Plant species discrimination in a tropical wetland using in situ hyperspectral data","volume":"6","author":"Prospere","year":"2014","journal-title":"Remote Sens."},{"key":"ref_27","first-page":"29","article-title":"Compensation of hyperspectral data for atmospheric effects","volume":"14","author":"Griffin","year":"2003","journal-title":"Linc. Lab. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1364\/AO.39.000887","article-title":"Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space","volume":"39","author":"Gao","year":"2000","journal-title":"Appl. Opt."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.rse.2007.12.015","article-title":"Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean","volume":"113","author":"Gao","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"99","DOI":"10.5589\/m02-085","article-title":"Spectral unmixing of hyperspectral imagery for mineral exploration: Comparison of results from SFSI and AVIRIS","volume":"29","author":"Neville","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2409","DOI":"10.1080\/01431161003698336","article-title":"Hyperspectral remote sensing for mineral exploration in Pulang, Yunnan Province, China","volume":"32","author":"Bishop","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.1364\/AO.38.003831","article-title":"Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization","volume":"38","author":"Lee","year":"1999","journal-title":"Appl. Opt."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1109\/TGRS.2003.812907","article-title":"Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality","volume":"41","author":"Brando","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4009","DOI":"10.1016\/j.rse.2008.06.002","article-title":"Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin","volume":"112","author":"Randolph","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yin, Q., Li, H., and Hu, B. (2016, January 20\u201322). Surface ship target detection in hyperspectral images based on improved variance minimum algorithm. Proceedings of the SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), Chengu, China.","DOI":"10.1117\/12.2243872"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yan, L., Noro, N., Takara, Y., Ando, F., and Yamaguchi, M. (2015, January 15). Using hyperspectral image enhancement method for small size object detection on the sea surface. Proceedings of the SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430H, Toulouse, France.","DOI":"10.1117\/12.2194606"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"535","DOI":"10.5467\/JKESS.2017.38.7.535","article-title":"The Ship Detection Using Airborne and In-situ Measurements Based on Hyperspectral Remote Sensing","volume":"38","author":"Park","year":"2017","journal-title":"J. Korean Earth Sci. Soc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1109\/LGRS.2005.846011","article-title":"On the impact of PCA dimension reduction for hyperspectral detection of difficult targets","volume":"2","author":"Farrell","year":"2005","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1080\/07038992.2016.1160772","article-title":"Minimum noise fraction versus principal component analysis as a preprocessing step for hyperspectral imagery denoising","volume":"42","author":"Luo","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"248","DOI":"10.7763\/IJMLC.2012.V2.124","article-title":"Band selection for dimension reduction in hyper spectral image using integrated information gain and principal components analysis technique","volume":"2","author":"Koonsanit","year":"2012","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_42","unstructured":"Descour, M.R., and Shen, S.S. (1999). N-FINDR: An Algorithm for Fast Autonomous Spectral End-Member Determination in Hyperspectral Data, Imaging Spectrometry V."},{"key":"ref_43","first-page":"545","article-title":"Fast algorithms to implement N-FINDR for hyperspectral endmember extraction","volume":"4","author":"Xiong","year":"2011","journal-title":"IEEE J. STARS"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5928","DOI":"10.1080\/01431161.2019.1707904","article-title":"Applying hyperspectral remote sensing methods to ship detection based on airborne and ground experiments","volume":"41","author":"Park","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","unstructured":"Boardman, J.W., Kruse, F.A., and Green, R.O. (1995). Mapping Target Signatures via Partial Unmixing of AVIRIS Data. Summaries of the Fifth JPL Airborne Earth Science Workshop, NASA Jet Propulsion Laboratory. JPL Publication 95\u20131."},{"key":"ref_46","first-page":"29","article-title":"Pixel purity index-based algorithms for endmember extraction from hyperspectral imagery","volume":"Volume 37","author":"Chaudhry","year":"2006","journal-title":"Recent Advances in Hyperspectral Signal and Image Processing"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TGRS.2005.863297","article-title":"Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis","volume":"46","author":"Wang","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/TGRS.2004.839806","article-title":"Does independent component analysis play a role in unmixing hyperspectral data?","volume":"43","author":"Nascimento","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"59820L","DOI":"10.1117\/12.620061","article-title":"Estimation of signal subspace on hyperspectral data","volume":"5982","author":"Nascimento","year":"2005","journal-title":"Proc. SPIE"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TGRS.2005.844293","article-title":"Vertex component analysis: A fast algorithm to unmix hyperspectral data","volume":"43","author":"Nascimento","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","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":"2011","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s12601-012-0033-3","article-title":"Spatial scales of mesoscale eddies from GOCI Chlorophyll-a concentration images in the East\/Japan Sea","volume":"47","author":"Park","year":"2012","journal-title":"Ocean Sci. J."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/LGRS.2008.915928","article-title":"Receiver operating characteristic curve confidence intervals and regions","volume":"5","author":"Kerekes","year":"2008","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5905","DOI":"10.1080\/01431161.2019.1711246","article-title":"Estimation of ship size from satellite optical image using elliptic characteristics of ship periphery","volume":"41","author":"Park","year":"2020","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2968\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:09:18Z","timestamp":1760177358000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2968"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,11]]},"references-count":55,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12182968"],"URL":"https:\/\/doi.org\/10.3390\/rs12182968","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,11]]}}}