Classification of land use and vegetation with convolutional neural networks
DOI:
https://doi.org/10.29298/rmcf.v13i74.1269Keywords:
automatic classification, Atoyac-Salado River, machine learning, Sentinel-2 imagesAbstract
The classification of land use and vegetation is a complex and difficult exercise to perform with traditional methods, so deep learning models are an alternative for its application because they are highly capable of learning this complex semantics, which makes plausible its application in the automatic identification of land use and vegetation from spatio-temporal patterns extracted from their appearance. The objective was to propose and evaluate a deep learning convolutional neural network model for the classification of 22 different land cover and land use classes within the Atoyac-Salado river basin. The proposed model was trained using digital data captured in 2021 by the Sentinel 2 satellite, applying a different combination of hyperparameters, where the model accuracy depends on the optimizer, activation function, filter size, learning rate and batch size. The results provided an accuracy of 84.57% for the data set. To reduce overfitting, a regularization method called dropout was employed and proved to be very effective.
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