{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T13:44:11Z","timestamp":1774878251745,"version":"3.50.1"},"reference-count":20,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2012,11,1]],"date-time":"2012-11-01T00:00:00Z","timestamp":1351728000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.ieee.org\/publications_standards\/publications\/subscriptions\/info\/licensing.html"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["0705836"],"award-info":[{"award-number":["0705836"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,11]]},"abstract":"<jats:p>Endmember extraction and unmixing methods that exploit nonlinearity in hyperspectral data are receiving increased attention, but they have significant challenges. Global feature extraction methods such as isometric feature mapping have significant computational overhead, which is often addressed for the classification problem via landmark-based methods. Because landmark approaches are approximation methods, experimental results are often highly variable. We propose a new robust landmark selection method for the purpose of pixel unmixing that exploits spectral and spatial homogeneity in a local window kernel. We compare the performance of the method to several landmark selection methods in terms of reconstruction error and processing time.<\/jats:p>","DOI":"10.1109\/lgrs.2012.2219613","type":"journal-article","created":{"date-parts":[[2012,10,26]],"date-time":"2012-10-26T14:03:13Z","timestamp":1351260193000},"page":"711-715","source":"Crossref","is-referenced-by-count":18,"title":["Selection of Landmark Points on Nonlinear Manifolds for Spectral Unmixing Using Local Homogeneity"],"prefix":"10.1109","volume":"10","author":[{"given":"Junhwa","family":"Chi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Melba","family":"Crawford","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1117\/12.366289","article-title":"N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data","volume":"3753","author":"winter","year":"1999","journal-title":"Proc SPIE Image Spectrometry V"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A global geometric framework for nonlinear dimensionality reduction","volume":"290","author":"tenenbaum","year":"2000","journal-title":"Science"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by local linear embedding","volume":"290","author":"roweis","year":"2000","journal-title":"Science"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2010.2088377"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2004.842292"},{"key":"ref15","author":"silva","year":"2004","journal-title":"Sparse multidimensional scaling using landmark points"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2006.144"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2006.881801"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/0020-0255(73)90043-1"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2003.819189"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2003.1293753"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/79.974727"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2002.802494"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2003.820314"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2046671"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2007.02.019"},{"key":"ref2","year":"0","journal-title":"The University of Texas at Austin\/Center for Space Research"},{"key":"ref1","year":"0"},{"key":"ref9","author":"kim","year":"2011","journal-title":"Manifold learning for robust classification of hyperspectral data"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/36.911111"}],"container-title":["Geoscience and Remote Sensing Letters, IEEE"],"original-title":[],"link":[{"URL":"http:\/\/ieeexplore.ieee.org\/iel5\/8859\/6365805\/06341046.pdf?arnumber=6341046","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx5\/8859\/6365805\/06341046.pdf?arnumber=6341046","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,20]],"date-time":"2017-06-20T21:55:50Z","timestamp":1497995750000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/6341046\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,11]]},"references-count":20,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2012,11]]}},"URL":"https:\/\/doi.org\/10.1109\/lgrs.2012.2219613","relation":{},"ISSN":["1545-598X"],"issn-type":[{"value":"1545-598X","type":"print"}],"subject":[],"published":{"date-parts":[[2012,11]]}}}