Generating land-cover maps from remotely sensed data: Manual vectorization versus object-oriented automation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F15%3A43906208" target="_blank" >RIV/62156489:43410/15:43906208 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Generating land-cover maps from remotely sensed data: Manual vectorization versus object-oriented automation

Popis výsledku v původním jazyce

Manual vectorization of multispectral images is a widely used method for making land-use or land-cover maps. Although it is usually considered relatively accurate it is very time consuming, which has prompted the use in recent years of various semiautomatic methods for classifying remotely sensed images. One of the most promising of the latter is object-oriented image analysis based upon image segmentation, but the accuracy of its results, as well as its time demands, are disputed. Accordingly, this paper compared manual vectorization with object-oriented classification to reveal the strong and weak points of each. Two qualitatively different datasets were classified using both methods; time costs were monitored and accuracy levels were compared. It was found that manual vectorization achieved better overall accuracy (up to 93% versus 84%), but the semiautomatic method was usually more accurate when classifying some specific features such as roads, built-up areas, broadleaf trees and c

Název v anglickém jazyce

Generating land-cover maps from remotely sensed data: Manual vectorization versus object-oriented automation

Popis výsledku anglicky

Manual vectorization of multispectral images is a widely used method for making land-use or land-cover maps. Although it is usually considered relatively accurate it is very time consuming, which has prompted the use in recent years of various semiautomatic methods for classifying remotely sensed images. One of the most promising of the latter is object-oriented image analysis based upon image segmentation, but the accuracy of its results, as well as its time demands, are disputed. Accordingly, this paper compared manual vectorization with object-oriented classification to reveal the strong and weak points of each. Two qualitatively different datasets were classified using both methods; time costs were monitored and accuracy levels were compared. It was found that manual vectorization achieved better overall accuracy (up to 93% versus 84%), but the semiautomatic method was usually more accurate when classifying some specific features such as roads, built-up areas, broadleaf trees and c

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

GK - Lesnictví

OECD FORD obor

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Projekt

<a href="/cs/project/ED3.1.00%2F10.0220" target="_blank" >ED3.1.00/10.0220: Centrum Transferu Technologií MENDELU</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Applied GIS

ISSN

1832-5505

e-ISSN

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Svazek periodika

11

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

AU - Austrálie

Počet stran výsledku

30

Strana od-do

1-30

Kód UT WoS článku

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EID výsledku v databázi Scopus

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