Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances
The success of the supervised classification of remotely sensed images acquired over large geographical areas or at short time intervals strongly depends on the representativity of the samples used to train the classification algorithm and to define the model. When training samples are collected fro...
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| Published in | IEEE geoscience and remote sensing magazine Vol. 4; no. 2; pp. 41 - 57 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.06.2016
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| Subjects | |
| Online Access | Get full text |
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| Abstract | The success of the supervised classification of remotely sensed images acquired over large geographical areas or at short time intervals strongly depends on the representativity of the samples used to train the classification algorithm and to define the model. When training samples are collected from an image or a spatial region that is different from the one used for mapping, spectral shifts between the two distributions are likely to make the model fail. Such shifts are generally due to differences in acquisition and atmospheric conditions or to changes in the nature of the object observed. To design classification methods that are robust to data set shifts, recent remote sensing literature has considered solutions based on domain adaptation (DA) approaches. Inspired by machine-learning literature, several DA methods have been proposed to solve specific problems in remote sensing data classification. This article provides a critical review of the recent advances in DA approaches for remote sensing and presents an overview of DA methods divided into four categories: 1) invariant feature selection, 2) representation matching, 3) adaptation of classifiers, and 4) selective sampling. We provide an overview of recent methodologies, examples of applications of the considered techniques to real remote sensing images characterized by very high spatial and spectral resolution as well as possible guidelines for the selection of the method to use in real application scenarios. |
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| AbstractList | The success of the supervised classification of remotely sensed images acquired over large geographical areas or at short time intervals strongly depends on the representativity of the samples used to train the classification algorithm and to define the model. When training samples are collected from an image or a spatial region that is different from the one used for mapping, spectral shifts between the two distributions are likely to make the model fail. Such shifts are generally due to differences in acquisition and atmospheric conditions or to changes in the nature of the object observed. To design classification methods that are robust to data set shifts, recent remote sensing literature has considered solutions based on domain adaptation (DA) approaches. Inspired by machine-learning literature, several DA methods have been proposed to solve specific problems in remote sensing data classification. This article provides a critical review of the recent advances in DA approaches for remote sensing and presents an overview of DA methods divided into four categories: 1) invariant feature selection, 2) representation matching, 3) adaptation of classifiers, and 4) selective sampling. We provide an overview of recent methodologies, examples of applications of the considered techniques to real remote sensing images characterized by very high spatial and spectral resolution as well as possible guidelines for the selection of the method to use in real application scenarios. |
| Author | Tuia, Devis Bruzzone, Lorenzo Persello, Claudio |
| Author_xml | – sequence: 1 givenname: Devis surname: Tuia fullname: Tuia, Devis email: devis.tuia@geo.uzh.ch organization: Dept. of Geogr., Univ. of Zurich, Zurich, Switzerland – sequence: 2 givenname: Claudio surname: Persello fullname: Persello, Claudio email: c.persello@utwente.nl organization: Fac. Geo-Inf. Sci. & Earth Obs., Univ. of Twente, Enschede, Netherlands – sequence: 3 givenname: Lorenzo surname: Bruzzone fullname: Bruzzone, Lorenzo email: lorenzo.bruzzone@disi.unitn.it organization: Univ. of Genoa, Genoa, Italy |
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| CODEN | IGRSCZ |
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| Cites_doi | 10.1109/TGRS.2012.2198654 10.1080/13658816.2013.865189 10.1109/TGRS.2012.2195727 10.1016/j.isprsjprs.2014.03.016 10.1016/S1566-2535(02)00091-X 10.1109/TKDE.2009.191 10.1109/JPROC.2012.2231951 10.1109/TGRS.2012.2192740 10.1109/MSP.2014.2347059 10.1109/TGRS.2012.2200043 10.1109/TGRS.2015.2449736 10.1109/JSTSP.2011.2139193 10.1016/j.jag.2012.12.004 10.1109/TGRS.2006.878442 10.1109/JSTARS.2015.2420582 10.1109/LGRS.2012.2236818 10.1007/978-3-642-15561-1_16 10.1109/JSTARS.2012.2202881 10.1109/JSTARS.2015.2500961 10.1109/LGRS.2013.2255258 10.1109/TGRS.2009.2019636 10.1109/LGRS.2015.2391297 10.1016/j.isprsjprs.2015.05.004 10.1109/LGRS.2012.2227297 10.1109/JSTARS.2014.2302333 10.2307/1912352 10.1109/TGRS.2014.2317499 10.1016/j.isprsjprs.2015.02.005 10.1109/TGRS.2013.2295819 10.1109/TGRS.2006.877950 10.1016/j.rse.2004.12.015 10.1109/TIP.2006.888195 10.1109/TGRS.2007.894550 10.1109/JPROC.2015.2449668 10.1109/LGRS.2015.2512999 10.1016/S0167-8655(02)00053-3 10.1016/j.rse.2011.04.022 10.1109/IGARSS.2008.4778790 10.1109/TGRS.2011.2105490 10.1109/LGRS.2012.2220516 10.1145/1015330.1015425 10.1109/TGRS.2012.2200045 10.1109/JSTARS.2012.2222356 10.1201/b11656-18 10.1109/TGRS.2013.2249522 10.1109/JSTARS.2015.2449738 10.1109/TGRS.2014.2377785 10.1109/TGRS.2014.2305805 10.1109/TGRS.2007.912445 10.1109/TPAMI.2009.57 10.1109/WACV.2013.6475043 10.1109/TGRS.2002.803794 10.1371/journal.pone.0148655 10.1109/TGRS.2007.910220 10.1109/TGRS.2013.2246837 10.1109/TGRS.2004.842481 10.1109/TGRS.2014.2300189 10.1109/TGRS.2011.2174154 10.1016/j.rse.2012.03.013 10.1016/j.rse.2010.10.011 10.7551/mitpress/9780262170055.001.0001 10.1016/j.rse.2011.10.014 10.1016/j.patcog.2010.09.013 10.1007/978-3-031-01560-1 10.1109/IGARSS.2011.6049404 10.1109/TGRS.2015.2503885 10.7551/mitpress/9780262017091.001.0001 10.1117/12.829645 10.1109/36.905255 10.1109/TGRS.2010.2076287 10.1109/TGRS.2011.2168534 10.1109/LGRS.2008.916070 10.1109/LGRS.2015.2491605 |
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| References | ref57 ref13 ref56 ref12 ref59 ref15 ref58 marcos gonzalez (ref40) 0 ref14 ref53 ref52 ref55 ref54 ref10 ref17 ref16 ref19 ref18 ref51 settles (ref68) 2012 ref50 sun (ref60) 2013; 10 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 stumpf (ref73) 0 zhang (ref46) 2014 ref35 ref78 ref34 ref37 ref36 ref75 ref31 ref74 ref30 ref77 ref33 ref76 ref32 ref2 ref1 ref39 ref38 ref71 ref70 ref72 luo (ref79) 2005; 6 fleming (ref11) 1975 ref24 ref67 ref23 ref26 ref69 ref64 ref20 ref63 ref66 ref22 ref65 ref21 ref28 ref27 ref29 huang (ref25) 0 ref62 ref61 |
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