Hyperspectral Endmember Extraction by (μ + λ) Multiobjective Differential Evolution Algorithm Based on Ranking Multiple Mutations

Endmember extraction (EE) plays a crucial part in the hyperspectral unmixing (HU) process. To obtain satisfactory EE results, the EE can be considered as the multiobjective optimization problem to optimize the volume maximization (VM) and root-mean-square error (RMSE) simultaneously. However, it is...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 59; číslo 3; s. 2352 - 2364
Hlavní autori:	Tong, Lyuyang, Du, Bo, Liu, Rong, Zhang, Liangpei, Tan, Kay Chen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.03.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:	0196-2892, 1558-0644
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Abstract	Endmember extraction (EE) plays a crucial part in the hyperspectral unmixing (HU) process. To obtain satisfactory EE results, the EE can be considered as the multiobjective optimization problem to optimize the volume maximization (VM) and root-mean-square error (RMSE) simultaneously. However, it is often quite challenging to balance the conflict of these objectives. In order to tackle the challenges of multiobjective EE, we present a <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula> multiobjective differential evolution algorithm (<inline-formula> <tex-math notation="LaTeX">({\mu +\lambda }) </tex-math></inline-formula>-MODE) based on ranking multiple mutations. In the <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula>-MODE algorithm, ranking multiple mutations are adopted to create the mutant vectors via the scaling factor pool to enhance the population diversity. Moreover, mutant vectors employ the binary crossover operator to generate the trial vectors through a crossover control parameter pool in <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula>-MODE to take advantage of the good information of the population. In addition, <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula>-MODE utilizes the fast nondominated sorting approach to sort the parent and trial vectors, and then selects the elitism offspring as the next population via the <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula> selection strategy. Eventually, experimental comparative results in three real HSIs reveal that our proposed <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula>-MODE is superior to other EE methods.
AbstractList	Endmember extraction (EE) plays a crucial part in the hyperspectral unmixing (HU) process. To obtain satisfactory EE results, the EE can be considered as the multiobjective optimization problem to optimize the volume maximization (VM) and root-mean-square error (RMSE) simultaneously. However, it is often quite challenging to balance the conflict of these objectives. In order to tackle the challenges of multiobjective EE, we present a [Formula Omitted] multiobjective differential evolution algorithm ([Formula Omitted]-MODE) based on ranking multiple mutations. In the [Formula Omitted]-MODE algorithm, ranking multiple mutations are adopted to create the mutant vectors via the scaling factor pool to enhance the population diversity. Moreover, mutant vectors employ the binary crossover operator to generate the trial vectors through a crossover control parameter pool in [Formula Omitted]-MODE to take advantage of the good information of the population. In addition, [Formula Omitted]-MODE utilizes the fast nondominated sorting approach to sort the parent and trial vectors, and then selects the elitism offspring as the next population via the [Formula Omitted] selection strategy. Eventually, experimental comparative results in three real HSIs reveal that our proposed [Formula Omitted]-MODE is superior to other EE methods. Endmember extraction (EE) plays a crucial part in the hyperspectral unmixing (HU) process. To obtain satisfactory EE results, the EE can be considered as the multiobjective optimization problem to optimize the volume maximization (VM) and root-mean-square error (RMSE) simultaneously. However, it is often quite challenging to balance the conflict of these objectives. In order to tackle the challenges of multiobjective EE, we present a <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula> multiobjective differential evolution algorithm (<inline-formula> <tex-math notation="LaTeX">({\mu +\lambda }) </tex-math></inline-formula>-MODE) based on ranking multiple mutations. In the <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula>-MODE algorithm, ranking multiple mutations are adopted to create the mutant vectors via the scaling factor pool to enhance the population diversity. Moreover, mutant vectors employ the binary crossover operator to generate the trial vectors through a crossover control parameter pool in <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula>-MODE to take advantage of the good information of the population. In addition, <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula>-MODE utilizes the fast nondominated sorting approach to sort the parent and trial vectors, and then selects the elitism offspring as the next population via the <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula> selection strategy. Eventually, experimental comparative results in three real HSIs reveal that our proposed <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula>-MODE is superior to other EE methods.
Author	Tan, Kay Chen Zhang, Liangpei Du, Bo Tong, Lyuyang Liu, Rong
Author_xml	– sequence: 1 givenname: Lyuyang orcidid: 0000-0001-7148-3618 surname: Tong fullname: Tong, Lyuyang email: lyuyangtong@outlook.com organization: School of Computer Science, Wuhan University, Wuhan, China – sequence: 2 givenname: Bo orcidid: 0000-0002-0059-8458 surname: Du fullname: Du, Bo email: dubo@whu.edu.cn organization: School of Computer Science, Wuhan University, Wuhan, China – sequence: 3 givenname: Rong orcidid: 0000-0002-4642-9086 surname: Liu fullname: Liu, Rong email: rong.liu@dlr.de organization: Germany Aerospace Center (DLR), Weßling, Germany – sequence: 4 givenname: Liangpei orcidid: 0000-0001-6890-3650 surname: Zhang fullname: Zhang, Liangpei email: zlp62@whu.edu.cn organization: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China – sequence: 5 givenname: Kay Chen orcidid: 0000-0002-6802-2463 surname: Tan fullname: Tan, Kay Chen email: kaytan@cityu.edu.hk organization: Department of Computer Science, City University of Hong Kong, Hong Kong
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Cites_doi	10.1016/j.swevo.2011.03.001 10.1109/LGRS.2013.2285476 10.1109/TGRS.2019.2917001 10.1109/TGRS.2005.844293 10.1109/TIP.2018.2878958 10.1109/JSTSP.2018.2877497 10.1016/j.isprsjprs.2013.11.014 10.1109/79.974727 10.1109/JSTARS.2014.2373491 10.1162/EVCO_a_00024 10.1109/TIP.2014.2363423 10.1109/JSTARS.2015.2427656 10.1109/TCYB.2013.2239988 10.1109/TGRS.2011.2131145 10.1109/TGRS.2010.2098413 10.1109/JSTSP.2015.2419184 10.1109/TCYB.2014.2334692 10.1109/NAFIPS.1996.534790 10.1109/JSTARS.2014.2371615 10.1137/17M114145X 10.1109/JSTARS.2016.2640274 10.1109/JSTARS.2012.2194696 10.1109/TEVC.2010.2059031 10.1109/MGRS.2013.2244672 10.1109/TEVC.2010.2087271 10.1117/12.366289 10.1109/TGRS.2016.2580702 10.1109/JSTARS.2013.2242255 10.1016/j.neucom.2018.02.038 10.1109/JSTARS.2013.2267204 10.1109/TGRS.2011.2108305 10.3390/rs9030197 10.3390/rs9060558 10.1109/4235.996017 10.1016/j.swevo.2016.01.004 10.1109/TGRS.2019.2899826 10.1126/science.228.4704.1147 10.1109/NAFIPS.1996.534789 10.1109/TCYB.2015.2493239 10.1002/(SICI)1099-128X(199709/10)11:5<393::AID-CEM483>3.3.CO;2-C 10.1023/A:1008202821328 10.1109/ACCESS.2018.2856832 10.1016/j.ins.2012.01.017
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References	ref13 ref34 ref37 ref15 ref36 ref14 ref31 ref30 ref33 ref32 ref10 winter (ref11) 1999; 3753 boardman (ref12) 1995; 95 ref2 ref1 ref39 chen (ref35) 2010 ref17 ref38 ref16 ref19 ref18 zhu (ref44) 2014 ref46 ref24 ref45 ref23 ref26 ref25 ref20 ref42 ref41 ref22 ref21 ref43 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40
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Snippet	Endmember extraction (EE) plays a crucial part in the hyperspectral unmixing (HU) process. To obtain satisfactory EE results, the EE can be considered as the...
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Title	Hyperspectral Endmember Extraction by (μ + λ) Multiobjective Differential Evolution Algorithm Based on Ranking Multiple Mutations
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