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Super-resolution mapping of wetland inundation from remote sensing imagery based on integration of back-propagation neural network and genetic algorithm

Li, Linyi; Chen, Yun; Xu, Tingbao; Liu, Rui; Shi, Kaifang; Huang, Chang

Description

Mapping the spatio-temporal characteristics of wetland inundation has an important significance to the study of wetland environment and associated flora and fauna. High temporal remote sensing imagery is widely used for this purpose with the limitations of relatively low spatial resolutions. In this study, a novel method based on integration of back-propagation neural network (BP) and genetic algorithm (GA), so-called IBPGA, is proposed for super-resolution mapping of wetland inundation (SMWI)...[Show more]

dc.contributor.authorLi, Linyi
dc.contributor.authorChen, Yun
dc.contributor.authorXu, Tingbao
dc.contributor.authorLiu, Rui
dc.contributor.authorShi, Kaifang
dc.contributor.authorHuang, Chang
dc.date.accessioned2015-09-09T05:54:08Z
dc.date.available2015-09-09T05:54:08Z
dc.identifier.issn0034-4257
dc.identifier.urihttp://hdl.handle.net/1885/15291
dc.description.abstractMapping the spatio-temporal characteristics of wetland inundation has an important significance to the study of wetland environment and associated flora and fauna. High temporal remote sensing imagery is widely used for this purpose with the limitations of relatively low spatial resolutions. In this study, a novel method based on integration of back-propagation neural network (BP) and genetic algorithm (GA), so-called IBPGA, is proposed for super-resolution mapping of wetland inundation (SMWI) from multispectral remote sensing imagery. The IBPGA-SMWI algorithm is developed, including the fitness function and integration search strategy. IBPGA-SMWI was evaluated using Landsat TM/ETM + imagery from the Poyanghu wetland in China and the Macquarie Marshes in Australia. Compared with traditional SMWI methods, IBPGA-SMWI consistently achieved more accurate super-resolution mapping results in terms of visual and quantitative evaluations. In comparison with GA-SMWI, IBPGA-SMWI not only improved the accuracy of SMWI, but also accelerated the convergence speed of the algorithm. The sensitivity analysis of IBPGA-SMWI in relation to standard crossover rate, BP crossover rate and mutation rate was also carried out to discuss the algorithm performance. It is hoped that the results of this study will enhance the application of median-low resolution remote sensing imagery in wetland inundation mapping and monitoring, and ultimately support the studies of wetland environment.
dc.description.sponsorshipThis paper was supported by the National Natural Science Foundation of China (Grant No. 41371343 and Grant No. 41001255) and the scholarship provided by the China Scholarship Council (Grant No. 201308420290).
dc.publisherElsevier
dc.rights© 2015 Elsevier Inc. http://www.sherpa.ac.uk/romeo/issn/0034-4257/..."Authors pre-print on any website, including arXiv and RePEC" from SHERPA/RoMEO site (as at 11/09/15).
dc.sourceRemote Sensing of Environment
dc.subjectWetland inundation
dc.subjectSuper-resolution mapping
dc.subjectIntelligent algorithm integration
dc.subjectRemote sensing imagery
dc.titleSuper-resolution mapping of wetland inundation from remote sensing imagery based on integration of back-propagation neural network and genetic algorithm
dc.typeJournal article
local.identifier.citationvolume164
dcterms.dateAccepted2015-04-09
dc.date.issued2015-07
local.identifier.absfor210302 - Asian History
local.identifier.absfor210307 - European History (excl. British, Classical Greek and Roman)
local.identifier.ariespublicationU3488905xPUB7457
local.publisher.urlhttp://www.elsevier.com/
local.type.statusSubmitted Version
local.contributor.affiliationXu, T., Fenner School of Environment and Society, The Australian National University
local.bibliographicCitation.issue2015
local.bibliographicCitation.startpage142
local.bibliographicCitation.lastpage154
local.identifier.doi10.1016/j.rse.2015.04.009
dc.date.updated2016-02-24T10:13:18Z
local.identifier.scopusID2-s2.0-84928653893
CollectionsANU Research Publications

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