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primary topic: Neural network-based segmentation of satellite imagery for estimating house cluster of an urban settlement from Google Earth images

Neural network-based segmentation of satellite imagery for estimating house cluster of an urban settlement from Google Earth images
type: AcademicArticle
type: Article
abstract: In this paper a backpropagation neural network is utilized to perform house cluster segmentation from Google Earth data. The algorithm is subjected to identify houses in the image based on the RGB pattern within each pixel. Training data is given through cropping selection for a target that is a house cluster and a non object. The algorithm assigns 1 to a pixel belong to a class of object and 0 to a class of non object. The resulting outcome, a binary image, is then utilized to perform quantification to estimate the number of house clusters. The number of the hidden layer is varying in order to find its effect to the neural network performance and total computational time. © Published under licence by IOP Publishing Ltd.
date: 2014
volume: 18
issue: 1
is part of: IOP Conference Series: Earth and Environmental Science
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

Institute of Physics Publishing
type: Organization

IOP Conference Series: Earth and Environmental Science
type: Collection
issn: 17551307

P.D. Wardaya
type: Person
given name: P.D.
family name: Wardaya

S. Ridha
type: Person
given name: S.
family name: Ridha

HTML Summary of #5309 Neural network-based segmentation of satellite imagery for estimating house cluster of an urban settlement from Google Earth images
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primary topic: Neural network-based segmentation of satellite imagery for estimating house cluster of an urban settlement from Google Earth images