Automatic Classification of Pararubber Trees in Thailand from LANDSAT-8 Images Using Neural Network Method

Authors

  • C. Supunyachotsakul Department of Civil Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand https://orcid.org/0000-0003-1247-7255
  • N. Suksangpanya School of Geoinformatics, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, Thailand

DOI:

https://doi.org/10.13052/jmm1550-4646.17410

Keywords:

Feature classification, machine learning, neural network, satellite image.

Abstract

Classifying features from satellite images has been a time-consuming manual process which requires lots of manpower. This work exploits deep convolutional encoder-decoder neural network to develop an algorithm that can automatically classify the extents of the Pararubber tree-growing areas from the LANDSAT-8 images. The ground truth of the areas of the Pararubber tree was manually prepared and was separated into training datasets and the validation datasets. The classification model from this approach obtained using the training datasets was verified with the classification accuracy of70.90%, precision of 67.66%, recall of 80.80%, and F1 score of 73.59%.

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Author Biographies

C. Supunyachotsakul, Department of Civil Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

C. Supunyachotsakul received her B.Eng. degree in Survey Engineering from Chulalongkorn University, Thailand; and M.Sc. degrees in Photogrammetry and Geoinformatics from Stuttgart University of Applied Sciences, Germany; and M.S.E. and Ph.D. degrees in Civil Engineering from Purdue University, USA. Dr. Supunyachotsakul is currently a faculty member at Department of Civil Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand. Her research interest is in the areas of Photogrammetry, LIDAR technology, and 3D-point cloud data processing.

N. Suksangpanya, School of Geoinformatics, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, Thailand

N. Suksangpanya received his B.Eng. (2nd-class honor) and M.E. degrees in Mechanical Engineering from King Mongkut’s Institute of Technology Ladkrabang; and M.S.E. and Ph.D. degrees in Civil Engineering from Purdue University, USA. Dr. Suksangpanya is currently a faculty member of School of Geoinformatics, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, Thailand. His research interest is in the areas of image processing, feature extraction, and 3D-model reconstruction from point cloud data.

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Published

2021-06-21

Issue

Section

Smart Innovative Technology for Future Industry and Multimedia Applications