Hyperspectral image segmentation for estimation of biomass at reclaimed heaps

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Hyperspectral image segmentation for estimation of biomass at reclaimed heaps

Popis výsledku v původním jazyce

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 forestspecies 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 MLclassifi 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

Název v anglickém jazyce

Hyperspectral image segmentation for estimation of biomass at reclaimed heaps

Popis výsledku anglicky

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 forestspecies 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 MLclassifi 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

Druh

D - Stať ve sborníku

CEP obor

EH - Ekologie – společenstva

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Global Change and Resilience: From Impacts to Responses : Proceedings of the 3rd annual Global Change and Resilience Conference

ISBN

978-80-904351-8-6

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

200-203

Název nakladatele

Global change research centre, Academy of Sciences of the Czech Republic, v. v. i

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

22. 5. 2013

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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