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Statements

Subject Item
n2:RIV%2F67179843%3A_____%2F13%3A00423994%21RIV14-MSM-67179843
rdf:type
skos:Concept n18:Vysledek
dcterms:description
This paper presents the preliminary results from a study that aims at estimation of above ground biomass and soil carbon content at reclaimed mining heaps in the Sokolov region. Two image segmentation methods are presented. We applied maximal likelihood (ML) and neural network (NN) classifi ers on airborne hyperspectral data. Th e objective of this part of the study was to prepare a land cover classifi cation of the region. Th e main focus was paid to discrimination of six classes with prevailing forest species cover. Th e classifi cation accuracy of the training sites was 93.75 % for NN and 79.12 % for ML respectively. But ML outperformed NN in overall classifi cation accuracy with 61.54 % compared to 40.9 % of NN. Th e more accurate results of the ML classifi er are probably infl uenced by properties of the training samples. Th e larger size of the training samples derived for ML enabled better representation of class histograms. Th e lower overall NN accuracy could result from high spatial resolution of HS data. This paper presents the preliminary results from a study that aims at estimation of above ground biomass and soil carbon content at reclaimed mining heaps in the Sokolov region. Two image segmentation methods are presented. We applied maximal likelihood (ML) and neural network (NN) classifi ers on airborne hyperspectral data. Th e objective of this part of the study was to prepare a land cover classifi cation of the region. Th e main focus was paid to discrimination of six classes with prevailing forest species cover. Th e classifi cation accuracy of the training sites was 93.75 % for NN and 79.12 % for ML respectively. But ML outperformed NN in overall classifi cation accuracy with 61.54 % compared to 40.9 % of NN. Th e more accurate results of the ML classifi er are probably infl uenced by properties of the training samples. Th e larger size of the training samples derived for ML enabled better representation of class histograms. Th e lower overall NN accuracy could result from high spatial resolution of HS data.
dcterms:title
Hyperspectral image segmentation for estimation of biomass at reclaimed heaps Hyperspectral image segmentation for estimation of biomass at reclaimed heaps
skos:prefLabel
Hyperspectral image segmentation for estimation of biomass at reclaimed heaps Hyperspectral image segmentation for estimation of biomass at reclaimed heaps
skos:notation
RIV/67179843:_____/13:00423994!RIV14-MSM-67179843
n18:predkladatel
n19:ico%3A67179843
n3:aktivita
n6:P n6:I
n3:aktivity
I, P(ED1.1.00/02.0073), P(LM2010007), P(OC09001)
n3:dodaniDat
n4:2014
n3:domaciTvurceVysledku
n20:7059256 n20:4674987
n3:druhVysledku
n13:D
n3:duvernostUdaju
n21:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
78549
n3:idVysledku
RIV/67179843:_____/13:00423994
n3:jazykVysledku
n12:eng
n3:klicovaSlova
hyperspectral; classification; maximal likehood; neural network
n3:klicoveSlovo
n7:hyperspectral n7:neural%20network n7:maximal%20likehood n7:classification
n3:kontrolniKodProRIV
[0742D86C4625]
n3:mistoKonaniAkce
Brno
n3:mistoVydani
Brno
n3:nazevZdroje
Global Change and Resilience: From Impacts to Responses : Proceedings of the 3rd annual Global Change and Resilience Conference
n3:obor
n15:EH
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n9:LM2010007 n9:ED1.1.00%2F02.0073 n9:OC09001
n3:rokUplatneniVysledku
n4:2013
n3:tvurceVysledku
Pikl, Miroslav Zemek, FrantiĊĦek
n3:typAkce
n16:EUR
n3:zahajeniAkce
2013-05-22+02:00
s:numberOfPages
4
n14:hasPublisher
Global change research centre, Academy of Sciences of the Czech Republic, v. v. i
n17:isbn
978-80-904351-8-6