A NEURAL NETS URBAN LAND COVER CLASSIFICATION: A CASE STUDY OF
BRNO (CZECHIA).

J 2015

A NEURAL NETS URBAN LAND COVER CLASSIFICATION: A CASE STUDY OF BRNO (CZECHIA).

KÝNOVÁ, Andrea a Petr DOBROVOLNÝ

Základní údaje

Originální název

A NEURAL NETS URBAN LAND COVER CLASSIFICATION: A CASE STUDY OF BRNO (CZECHIA).

Autoři

KÝNOVÁ, Andrea (203 Česká republika, domácí) a Petr DOBROVOLNÝ (203 Česká republika, domácí)

Vydání

Acta Universitatis Carolinae Geographica, 2015, 0300-5402

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

Zemský magnetismus, geodesie, geografie

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Kód RIV

RIV/00216224:14310/15:00085925

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.14712/23361980.2015.94

Klíčová slova anglicky

image classification; multilayer perceptron; urban land cover; ASTER

Štítky

AKR, rivok

Příznaky

Recenzováno

Změněno: 24. 3. 2016 09:16, Ing. Andrea Mikešková

Anotace

V originále

Accurate and updated land cover maps provide crucial basic information in a number of important enterprises, with sustainable development and regional planning far from the least of them. Remote sensing is probably the most efficient approach to obtaining a land cover map. However, certain intrinsic limitations limit the accuracy of automatic approaches to image classification. Classifications within highly heterogeneous urban areas are especially challenging. This study makes a presentation of multilayer perceptron (MLP), an artificial neural network (ANN), as an applicable approach to image classification. Optimal MLP architecture parameters were established by means of a training set. The resulting network was used to classify a sub-scene within ASTER imagery. The results were evaluated against a test dataset. The overall accuracy of classification was 94.8%. This is comparable to classification results from a maximum likelihood classifier (MLC) used for the same image. In built-up areas, MLP did not exaggerate built-up areas at the expense of other classes to the same extent as MLC.

Návaznosti

MUNI/A/0952/2013, interní kód MU

Název: Analýza, hodnocení a vizualizace globálních environmentálních změn v krajinné sféře Země (Akronym: AVIGLEZ)

Investor: Masarykova univerzita, Analýza, hodnocení a vizualizace globálních environmentálních změn v krajinné sféře Země, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

KÝNOVÁ, Andrea a Petr DOBROVOLNÝ. A NEURAL NETS URBAN LAND COVER CLASSIFICATION: A CASE STUDY OF BRNO (CZECHIA). Acta Universitatis Carolinae Geographica. 2015, roč. 50, č. 2, s. 153-163. ISSN 0300-5402. Dostupné z: https://dx.doi.org/10.14712/23361980.2015.94.

@article{1324278,
   author = {Kýnová, Andrea and Dobrovolný, Petr},
   article_number = {2},
   doi = {http://dx.doi.org/10.14712/23361980.2015.94},
   keywords = {image classification; multilayer perceptron; urban land cover; ASTER},
   language = {eng},
   issn = {0300-5402},
   journal = {Acta Universitatis Carolinae Geographica},
   title = {A NEURAL NETS URBAN LAND COVER CLASSIFICATION: A CASE STUDY OF BRNO (CZECHIA).},
   url = {http://www.aucgeographica.cz/index.php/AUC_Geographica/article/view/98/pdf_56},
   volume = {50},
   year = {2015}
}

TY  - JOUR
ID  - 1324278
AU  - Kýnová, Andrea - Dobrovolný, Petr
PY  - 2015
TI  - A NEURAL NETS URBAN LAND COVER CLASSIFICATION: A CASE STUDY OF BRNO (CZECHIA).
JF  - Acta Universitatis Carolinae Geographica
VL  - 50
IS  - 2
SP  - 153-163
EP  - 153-163
SN  - 03005402
KW  - image classification
KW  - multilayer perceptron
KW  - urban land cover
KW  - ASTER
UR  - http://www.aucgeographica.cz/index.php/AUC_Geographica/article/view/98/pdf_56
L2  - http://www.aucgeographica.cz/index.php/AUC_Geographica/article/view/98/pdf_56
N2  - Accurate and updated land cover maps provide crucial basic information in a number of important enterprises, with sustainable development and regional planning far from the least of them. Remote sensing is probably the most efficient approach to obtaining a land cover map. However, certain intrinsic limitations limit the accuracy of automatic approaches to image classification. Classifications within highly heterogeneous urban areas are especially challenging. This study makes a presentation of multilayer perceptron (MLP), an artificial neural network (ANN), as an applicable approach to image classification. Optimal MLP architecture parameters were established by means of a training set. The resulting network was used to classify a sub-scene within ASTER imagery. The results were evaluated against a test dataset. The overall accuracy of classification was 94.8%. This is comparable to classification results from a maximum likelihood classifier (MLC) used for the same image. In built-up areas, MLP did not exaggerate built-up areas at the expense of other classes to the same extent as MLC.
ER  -

KÝNOVÁ, Andrea a Petr DOBROVOLNÝ. A NEURAL NETS URBAN LAND COVER CLASSIFICATION: A CASE STUDY OF BRNO (CZECHIA). \textit{Acta Universitatis Carolinae Geographica}. 2015, roč.~50, č.~2, s.~153-163. ISSN~0300-5402. Dostupné z: https://dx.doi.org/10.14712/23361980.2015.94.

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