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dc.contributor.authorWanninayaka, WMR
dc.contributor.authorRathnayaka, RMKT
dc.contributor.authorUdayakumara, EPN
dc.date.accessioned2021-01-26T10:21:37Z
dc.date.available2021-01-26T10:21:37Z
dc.date.issued2020-10
dc.identifier.urihttp://ir.kdu.ac.lk/handle/345/3268
dc.description.abstractIn Sri Lanka, Seasonal paddy field area mapping is still doing based on the traditional methods with poor technologies. Therefore this research focuses on the machine approach of mapping paddy fields area accurately on remote sensing data taken from the satellite. Multi-temporal Sentinel1A Synthetic Aperture Radar(SAR) data was used to map the spatial distribution of the secretary’s divisions paddy area in the Ampara district during the period from April 2019 to September 2019. The classifying algorithms were mainly used under the multi-temporal spectral filter classification with 11 dual-polarization(VH/VV) SAR using SNAP, QGIS, ENVI tools. The Time series model was used for each VH and VV bands separately. According to minimum and maximum value of both VH and VV bands, paddy field area was classified using deference of min and max value respectively The overall precision of paddy fields is shown to be 0.92 Also use random forest classification method to processed images with ENVI and It shows 0.86 accuracy rate. Each divisional secretary area showed accurate paddy classification according to non-remote sensing data provided by the district agriculture office of Ampara. This method can easily be used to classify paddy cultivation areas than its traditional methods. Also, it is low cost and very fast method. As further development, Rice prediction model is proposed using the same classified area with vegetation indexes of Sentinel 2 imagery.en_US
dc.language.isoenen_US
dc.subjectRice Yielden_US
dc.subjectSentinel-1Aen_US
dc.subjectNearest Neighbouren_US
dc.subjectSARen_US
dc.subjectVVen_US
dc.subjectVHen_US
dc.subjectTime Seriesen_US
dc.titleMapping & Classifying Paddy Fields Applying Machine Learning Algorithms with Multi-temporal Sentinel-1A in Ampara districten_US
dc.typeArticle Full Texten_US
dc.identifier.journal13th International Research Conference General Sir John Kotelawala Defence Universityen_US
dc.identifier.pgnos167-175en_US


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