TY - JOUR AU - Supunyachotsakul, C. AU - Suksangpanya, N. PY - 2021/06/21 Y2 - 2024/03/28 TI - Automatic Classification of Pararubber Trees in Thailand from LANDSAT-8 Images Using Neural Network Method JF - Journal of Mobile Multimedia JA - JMM VL - 17 IS - 4 SE - Smart Innovative Technology for Future Industry and Multimedia Applications DO - 10.13052/jmm1550-4646.17410 UR - https://journals.riverpublishers.com/index.php/JMM/article/view/1007 SP - 693-706 AB - <p>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 <em>F<span id="MathJax-Element-1-Frame" class="MathJax" tabindex="0" role="presentation" data-mathml="<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot; id=&quot;S0.SSx1.p1.m1&quot; display=&quot;inline&quot;><msub><mi></mi><mn mathvariant=&quot;normal&quot;>1</mn></msub></math>"><span id="S0.SSx1.p1.m1" class="math"><span id="MathJax-Span-2" class="mrow"><span id="MathJax-Span-3" class="msub"><span id="MathJax-Span-4" class="mi"></span><span id="MathJax-Span-5" class="mn">1</span></span></span></span></span></em> score of 73.59%.</p> ER -