SCAPEVIEWER: preliminary results of a landscape perception classification system based on neural network technology

Stavroula, GM; Tsouchlaraki, AL; Cassios, C; Nikita, KS; Matsopoulos, GK; Uzunoglu, NK

dc.contributor.author	Stavroula, GM	en
dc.contributor.author	Tsouchlaraki, AL	en
dc.contributor.author	Cassios, C	en
dc.contributor.author	Nikita, KS	en
dc.contributor.author	Matsopoulos, GK	en
dc.contributor.author	Uzunoglu, NK	en
dc.date.accessioned	2014-03-01T01:54:47Z
dc.date.available	2014-03-01T01:54:47Z
dc.date.issued	2005	en
dc.identifier.issn	0925-8574	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27461
dc.subject	landscape perception	en
dc.subject	scenic beauty	en
dc.subject	classification	en
dc.subject	linear discrimination method	en
dc.subject	neural networks	en
dc.subject	genetic algorithm	en
dc.subject.classification	Ecology	en
dc.subject.classification	Engineering, Environmental	en
dc.subject.classification	Environmental Sciences	en
dc.subject.other	PREFERENCE	en
dc.title	SCAPEVIEWER: preliminary results of a landscape perception classification system based on neural network technology	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	In this paper, the implementation of a pilot computerized system for the classification of landscape images (SCAPEVIEWER) is presented. A total of 108 landscape photographs have been organized, according to the mean estimation of scenic beauty from seven experts, into three classes: indistinctive (C1), typical or common (C2), and distinctive (C3). For each of the landscape photographs, 10 indices are estimated. These indices are then fed to a classifier based on neural network (NN) technology. In order to examine whether NNs are suitable for this specific application, two different approaches have been tested and compared against a linear discrimination method (LDM) classifier. The first approach is a feed forward NN (Classic-NN), while the second approach (Hybrid-NN) is based on the Classic-NN modified by using genetic algorithms (GAs). The correct classification performances achieved by the Classic-NN and the Hybrid-NN were 87% and 84%, respectively, while the classification performance of the LDM classifier was only 68%. Although the Classic-NN achieved slightly better results than the Hybrid-NN, the latter is preferred due to its ability of index selection and automatical adjustment of internal NN parameters. The pilot system has shown the feasibility for classifying landscape photographs according to scenic beauty by means of a computerized system combining the knowledge of an expert with a NN classifier. (c) 2004 Elsevier B.V All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	ECOLOGICAL ENGINEERING	en
dc.identifier.isi	ISI:000229168600002	en
dc.identifier.volume	24	en
dc.identifier.issue	1-2	en
dc.identifier.spage	5	en
dc.identifier.epage	15	en