About: Hyperspectral image segmentation for estimation of biomass at reclaimed heaps

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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. (en)
Title	Hyperspectral image segmentation for estimation of biomass at reclaimed heaps Hyperspectral image segmentation for estimation of biomass at reclaimed heaps (en)
skos:prefLabel	Hyperspectral image segmentation for estimation of biomass at reclaimed heaps Hyperspectral image segmentation for estimation of biomass at reclaimed heaps (en)
skos:notation	RIV/67179843:_____/13:00423994!RIV14-MSM-67179843
http://linked.open...avai/riv/aktivita	I P
http://linked.open...avai/riv/aktivity	I, P(ED1.1.00/02.0073), P(LM2010007), P(OC09001)
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Pikl, Miroslav Zemek, František
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Centrum výzkumu globální změny AV ČR, v. v. i.
http://linked.open...dnocenehoVysledku	78549
http://linked.open...ai/riv/idVysledku	RIV/67179843:_____/13:00423994
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	hyperspectral; classification; maximal likehood; neural network (en)
http://linked.open.../riv/klicoveSlovo	classification neural network maximal likehood hyperspectral
http://linked.open...ontrolniKodProRIV	[0742D86C4625]
http://linked.open...v/mistoKonaniAkce	Brno
http://linked.open...i/riv/mistoVydani	Brno
http://linked.open...i/riv/nazevZdroje	Global Change and Resilience: From Impacts to Responses : Proceedings of the 3rd annual Global Change and Resilience Conference
http://linked.open...in/vavai/riv/obor	EH
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Remote sensing of forest recovery assessment after bark beetle outbreaks in the Bohemian Forest (Czech Republic) CzeCOS/ICOS - National Infrastructure of Carbon Observation System CzechGlobe - Center for Global Climate Change Impacts Studies
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Zemek, František Pikl, Miroslav
http://linked.open...vavai/riv/typAkce	EUR - Evropská
http://linked.open.../riv/zahajeniAkce	2013-05-22 (xsd:date)
number of pages	4 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Global change research centre, Academy of Sciences of the Czech Republic, v. v. i
https://schema.org/isbn	978-80-904351-8-6

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