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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F15%3A00085925" target="_blank" >RIV/00216224:14310/15:00085925 - isvavai.cz</a>

Result on the web

<a href="http://www.aucgeographica.cz/index.php/AUC_Geographica/article/view/98/pdf_56" target="_blank" >http://www.aucgeographica.cz/index.php/AUC_Geographica/article/view/98/pdf_56</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14712/23361980.2015.94" target="_blank" >10.14712/23361980.2015.94</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

DE - Earth magnetism, geodesy, geography

OECD FORD branch

—

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2015

Confidentiality

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

Name of the periodical

Acta Universitatis Carolinae Geographica

ISSN

0300-5402

e-ISSN

—

Volume of the periodical

50

Issue of the periodical within the volume

2

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

11

Pages from-to

153-163

UT code for WoS article

—

EID of the result in the Scopus database

—