Detection and clustering of features in aerial images by neuron network-based algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F15%3A33157585" target="_blank" >RIV/61989592:15310/15:33157585 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1117/12.2228918" target="_blank" >http://dx.doi.org/10.1117/12.2228918</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.2228918" target="_blank" >10.1117/12.2228918</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection and clustering of features in aerial images by neuron network-based algorithm

Popis výsledku v původním jazyce

The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of generalfeatures analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft. NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropria

Název v anglickém jazyce

Detection and clustering of features in aerial images by neuron network-based algorithm

Popis výsledku anglicky

The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of generalfeatures analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft. NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropria

Druh

D - Stať ve sborníku

CEP obor

DE - Zemský magnetismus, geodesie, geografie

OECD FORD obor

—

Projekt

<a href="/cs/project/EE2.3.20.0170" target="_blank" >EE2.3.20.0170: Budování výzkumně-vzdělávacího týmu v oblasti modelování přírodních jevů a využití geoinformačních systémů, s vazbou na zapojení do mezinárodních sítí a programů.</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of SPIE

ISBN

978-1-5106-0058-4

ISSN

0277-786X

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

SPIE

Místo vydání

Bellingham (OR)

Místo konání akce

Singapour

Datum konání akce

23. 10. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000367310300031