Supunyachotsakul, Chisaphat
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Preferred name
Supunyachotsakul, Chisaphat
Alternative Name
Supunyachotsakul, C.
Main Affiliation
Email
chisaphat.su@kmitl.ac.th
2 results
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Item type:Publication, Automatic para-rubber trees classification in Thailand from LANDSAT-8 imagery using DCED neural network(2021-06-02); Image 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Semi-Automatic Classification of Rotating Crops in Northern Thailand by Using Temporal LANDSAT Images(2022-01-01); This work focuses on rotating crops in the forest conservation areas in the northern region of Thailand which always cause false detection for forest encroachment and deforestation. Therefore, this work establishes a database of rotating crop areas in the northern region of Thailand and additionally develops a semi-automatic classification approach to help facilitate the classification process. LANDSAT images ranging from 1987 to 2018 are used as the input data for classifying the rotating crop areas. The semi-automatic classification approach is comprised of the automatic supervised classification and the manual classification by visual interpretation, respectively. The automatic and manual classification procedures are explained, and the results are verified by using ground truth locations distributed over the study region which gives 81.72% accuracy.