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dc.contributor.authorChristodoulou, Christodoulos I.en
dc.contributor.authorMichaelides, Silas C.en
dc.contributor.authorPattichis, Constantinos S.en
dc.contributor.authorKyriakou, Kyriakien
dc.creatorChristodoulou, Christodoulos I.en
dc.creatorMichaelides, Silas C.en
dc.creatorPattichis, Constantinos S.en
dc.creatorKyriakou, Kyriakien
dc.date.accessioned2019-11-13T10:39:17Z
dc.date.available2019-11-13T10:39:17Z
dc.date.issued2001
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/53724
dc.description.abstractThe aim of this work was to develop a system based on modular neural networks and multi-feature texture analysis that will facilitate the automated interpretation of cloud images. This will speed up the interpretation process and provide continuity in the application of satellite imagery for weather forecasting. A series of infrared satellite images from the Geostationary satellite METEOSAT7 were employed in this research. Nine different texture feature sets (a total of 55 features) were extracted from the segmented cloud images using the following algorithms: first order statistics, spatial gray level dependence matrices, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws texture energy measures, fractals, and Fourier power spectrum The neural network SOFM classifier and the statistical KNN classifier were used for the classification of the cloud images. Furthermore, the classification results of the different feature sets were combined improving the classification yield to 91%.en
dc.sourceIEEE International Conference on Image Processingen
dc.sourceIEEE International Conference on Image Processing (ICIP) 2001en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-0035167031&partnerID=40&md5=0fff5d50eb9124767d8c16f882cea8f4
dc.subjectStatistical methodsen
dc.subjectAlgorithmsen
dc.subjectFeature extractionen
dc.subjectNeural networksen
dc.subjectMatrix algebraen
dc.subjectFractalsen
dc.subjectImage analysisen
dc.subjectFourier transformsen
dc.subjectImage segmentationen
dc.subjectCloudsen
dc.subjectMultifeature texture analysisen
dc.subjectFirst order statisticsen
dc.subjectFourier power spectrumen
dc.subjectGray level difference statisticsen
dc.subjectNeighborhood gray tone difference matrixen
dc.subjectSatellite cloud imageryen
dc.subjectSpace researchen
dc.subjectSpatial gray level dependence matrixen
dc.subjectStatistical feature matrixen
dc.titleClassification of satellite cloud imagery based on multi-feature texture analysis and neural networksen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.description.volume1
dc.description.startingpage497
dc.description.endingpage500
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Sponsors: IEEEen
dc.description.notesConference code: 58800en
dc.description.notesCited By :1</p>en
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]


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