Automatic classification of pararubber trees in Thailand from LANDSAT-8 images using neural networks method

Abstract

Classifying features from satellite images has been a time-consuming manual process which requires lots of manpower. This work exploits deep convolutional decoder encoder neural network, to develop an algorithm that can automatically classify the extents of the Pararubber tree growing areas from the LANDSAT-8 images. The classification resulted from this approach was verified. In conclusion, the classification accuracy achieved is at 86.90% with Cohen's kappa at 73.80% which is considered satisfactory.