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Article: AUTOMATED RAIN-RATE CLASSIFICATION OF SATELLITE IMAGES USING STATISTICAL PATTERN RECOGNITION.

TitleAUTOMATED RAIN-RATE CLASSIFICATION OF SATELLITE IMAGES USING STATISTICAL PATTERN RECOGNITION.
Authors
KeywordsINFRARED IMAGING
PATTERN RECOGNITION
STATISTICAL METHODS
Issue Date1985
PublisherIEEE
Citation
Ieee Transactions On Geoscience And Remote Sensing, 1985, v. GE-23 n. 3, p. 315-323 How to Cite?
AbstractAn automated procedure to determine rain rates in visible and infrared satellite images by means of statistical pattern recognition is described. Using brightness and textural features extracted from the images, the procedure classifies 8 km multiplied by 8 km windows of data into one of three classes of rain rate: none, light, and heavy. The training process utilizes both weather radar and cloud-development information derived from image sequences. Images from three different days were tested and classification accuracies of 70% or better were obtained.
Persistent Identifierhttp://hdl.handle.net/10722/65519
ISSN
2015 Impact Factor: 3.36
2015 SCImago Journal Rankings: 1.975

 

DC FieldValueLanguage
dc.contributor.authorLee, Bonita Gen_HK
dc.contributor.authorChin, Roland Ten_HK
dc.contributor.authorMartin, David Wen_HK
dc.date.accessioned2010-08-31T07:15:02Z-
dc.date.available2010-08-31T07:15:02Z-
dc.date.issued1985en_HK
dc.identifier.citationIeee Transactions On Geoscience And Remote Sensing, 1985, v. GE-23 n. 3, p. 315-323en_HK
dc.identifier.issn0196-2892en_HK
dc.identifier.urihttp://hdl.handle.net/10722/65519-
dc.description.abstractAn automated procedure to determine rain rates in visible and infrared satellite images by means of statistical pattern recognition is described. Using brightness and textural features extracted from the images, the procedure classifies 8 km multiplied by 8 km windows of data into one of three classes of rain rate: none, light, and heavy. The training process utilizes both weather radar and cloud-development information derived from image sequences. Images from three different days were tested and classification accuracies of 70% or better were obtained.en_HK
dc.languageengen_HK
dc.publisherIEEEen_HK
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensingen_HK
dc.subjectINFRARED IMAGINGen_HK
dc.subjectPATTERN RECOGNITIONen_HK
dc.subjectSTATISTICAL METHODSen_HK
dc.titleAUTOMATED RAIN-RATE CLASSIFICATION OF SATELLITE IMAGES USING STATISTICAL PATTERN RECOGNITION.en_HK
dc.typeArticleen_HK
dc.identifier.emailChin, Roland T: rchin@hku.hken_HK
dc.identifier.authorityChin, Roland T=rp01300en_HK
dc.description.naturelink_to_subscribed_fulltexten_HK
dc.identifier.scopuseid_2-s2.0-0022068595en_HK
dc.identifier.volumeGE-23en_HK
dc.identifier.issue3en_HK
dc.identifier.spage315en_HK
dc.identifier.epage323en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLee, Bonita G=7405441068en_HK
dc.identifier.scopusauthoridChin, Roland T=7102445426en_HK
dc.identifier.scopusauthoridMartin, David W=35550605900en_HK

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