Abstract
Monitoring of Land use and Land cover (LULC) changes is a highly encumbering task for humans. Therefore, machine learning based classification systems can help to deal with this challenge. In this context, this study evaluates and compares the performance of two Single Learning (SL) techniques and one Ensemble Learning (EL) technique. All the empirical evaluations were over the open source LULC dataset proposed by the German Center for Artificial Intelligence (EuroSAT), and used the performance criteria -accuracy, precision, recall, F1 score and change in accuracy for the EL classifiers-. We firstly evaluate the performance of SL techniques: Building and optimizing a Convolutional Neural Network architecture, implementing Transfer learning, and training Machine learning algorithms on visual features extracted by Deep Feature Extractors. Second, we assess EL techniques and compare them with SL classifiers. Finally, we compare the capability of EL and hyperparameter tuning to improve the performance of the Deep Learning models we built. These experiments showed that Transfer learning is the SL technique that achieves the highest accuracy and that EL can indeed outperform the SL classifiers.
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Appendix A - Comparison of the Performance of Our Models on Moroccan LULC Images
Appendix A - Comparison of the Performance of Our Models on Moroccan LULC Images
We aim to use the models we built during this work to construct a Moroccan LULC dataset. Indeed, we will choose one of these models to be used as an annotator of Moroccan satellite images. Therefore, we decided to test these classifiers’ ability to generalize to images of Moroccan regions. In this appendix we present the classification results of the four classifiers (i.e. LULC-Net, VGG16, VGG16 + LR and DLEnsemble1). The nine images we tested our models on are of the city of Casablanca where Industrial, Residential, Sea & Lake and highway classes are present, and of the North of Morocco where classes such as Forest, Pasture, River and Annual Corp are present.
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LULC-Net
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VGG16
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VGG16 + LR
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DLEnsemble1
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Benbriqa, H., Abnane, I., Idri, A., Tabiti, K. (2021). Deep and Ensemble Learning Based Land Use and Land Cover Classification. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12951. Springer, Cham. https://doi.org/10.1007/978-3-030-86970-0_41
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DOI: https://doi.org/10.1007/978-3-030-86970-0_41
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