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dc.contributor.authorChristodoulou, Christodoulos I.en
dc.contributor.authorMichaelides, Silas C.en
dc.contributor.authorPattichis, Constantinos S.en
dc.creatorChristodoulou, Christodoulos I.en
dc.creatorMichaelides, Silas C.en
dc.creatorPattichis, Constantinos S.en
dc.date.accessioned2019-11-13T10:39:17Z
dc.date.available2019-11-13T10:39:17Z
dc.date.issued2003
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/53723
dc.description.abstractThe aim of this work was to develop a system based on multifeature texture analysis and modular neural networks that will facilitate the automated interpretation of satellite cloud images. Such a system will provide a standardized and efficient way for classifying cloud types that can he used as an operational tool in weather analysis. A series of 98 infrared satellite images from the geostationary satellite METEOSAT7 were employed, and 366 cloud segments were labeled into six cloud types after combined agreed observations from ground and satellite. From the segmented cloud images, nine different texture feature sets (a total of 55 features) were extracted, using the following algorithms: statistical features, 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 self-organizing feature map (SOFM) classifier and the statistical K-nearest neighbor (KNN) classifier were used for the classification of the cloud images. Furthermore, the classification results of the nine different feature sets were combined, improving the classification yield for the six classes, for the SOFM classifier to 61 % and for the KNN classifier to 64%.en
dc.sourceIEEE Transactions on Geoscience and Remote Sensingen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-0344014304&doi=10.1109%2fTGRS.2003.815404&partnerID=40&md5=9e006b362705d92a7355dc84533e08a9
dc.subjectStatistical methodsen
dc.subjectFeature extractionen
dc.subjectMatrix algebraen
dc.subjectSpectrum analysisen
dc.subjectClassificationen
dc.subjectClassification (of information)en
dc.subjectTextureen
dc.subjectRemote sensingen
dc.subjectFourier transformsen
dc.subjectSelf organizing mapsen
dc.subjectImage sensorsen
dc.subjectCloudsen
dc.subjectGeostationary satellitesen
dc.subjectK-nearest neighbor (KNN)en
dc.subjectMultifeature texture analysisen
dc.subjectSatellite imagesen
dc.subjectSelf-organizing feature map (SOFM)en
dc.titleMultifeature Texture Analysis for the Classification of Clouds in Satellite Imageryen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1109/TGRS.2003.815404
dc.description.volume41
dc.description.issue11 PART Ien
dc.description.startingpage2662
dc.description.endingpage2668
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :56</p>en
dc.source.abbreviationIEEE Trans.Geosci.Remote Sens.en
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]


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