Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine.
Published in ISPRS Open Journal of Photogrammetry and Remote Sensing, 2021
In this study, we used high-resolution satellite imagery from Planet that has been made available to the public through Norway’s International Climate and Forest Initiative (NICFI). We tested a U-Net deep-learning algorithm with a lightweight MobileNetV2 network as the encoder branch using the Google Earth Engine computational platform. We trained a model using the RGB channels with a pre-trained network (RGBt), an RGB model with randomly initialized weights (RGBr), and a model with randomly initialized weights including the NIR channel (RGBN).
Recommended citation: Poortinga, Ate, Nyein Soe Thwal, Nishanta Khanal, Timothy Mayer, Biplov Bhandari, Kel Markert, Andrea P. Nicolau et al. "Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine." ISPRS Open Journal of Photogrammetry and Remote Sensing 1 (2021): 100003.
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