Automatic para-rubber trees classification in Thailand from LANDSAT-8 imagery using DCED neural network

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dc.contributor.authorSupunyachotsakul, C.
dc.contributor.authorSuksangpanya, N.
dc.date.accessioned2026-08-06T10:32:54Z
dc.date.available2026-08-06T10:32:54Z
dc.date.issued2021-06-02
dc.description.abstractImage classification has been one of the main processes performed on satellite imagery where there have been many studies attempt to developing an automatic approach since manual classification is known to be a time-consuming process. Automatic classifying specific types of vegetation in satellite imagery has been a challenging field of study. In this work, a neural network method, specifically deep convolutional encoder-decoder or known as DCED, is applied to the automatic classifying coverage area of Para-rubber trees in Thailand from LANDSAT-8 satellite imagery. The main procedure in this work comprises ground truth preparation, suitable model development with training and validation, and, finally, classification. As a result, the model developed in this work gives average accuracy of 86.9% in training datasets and 70.9% in validation datasets.
dc.identifier.citationJournal of Mobile Multimedia, 17(4), 693-706, 2021
dc.identifier.doi10.13052/jmm1550-4646.17410
dc.identifier.issn15504646
dc.identifier.other2-s2.0-85114149750
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12122
dc.sourceJournal of Mobile Multimedia
dc.subjectAutomatic classification
dc.subjectDCED
dc.subjectNeural network
dc.subjectSatellite imagery
dc.titleAutomatic para-rubber trees classification in Thailand from LANDSAT-8 imagery using DCED neural network
dc.typeArticle

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