Automatic generation of a Portuguese land cover map with machine learning

• Autor(a) principal: Esteves, António
• Data de Publicação: 2024
• Outros Autores: Valente, Nuno
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/1822/89513
• topic: Machine learning; Deep learning; Remote sensing; Land cover map; Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática

The application of machine learning techniques to satellite imagery has been the subject of interest in recent years. The increase in quality and quantity of images, made available by Earth observation programs, such as the Copernicus program, led to the generation of large amounts of data. Among the various applications of this data is the creation of land cover maps. The present work aimed to create machine learning models capable of accurately segmenting and classifying satellite images to automatically generate a land cover map of the Portuguese territory. Several experiments were carried out with the spectral bands of the Sentinel-2 satellite, with vegetation indices, and with several sets of land cover classes. Three machine learning architectures were evaluated, which adopt two different techniques for image classification. One of the classification techniques follows an object-oriented approach, and in this case the architecture adopted in our models was a U-Net artificial neural network. The other classification technique is pixel-oriented, and the machine learning models tested were random forest and support vector machine. The overall accuracy of the results obtained ranged from 68.6% to 94.75%, depending strongly on the number of classes into which the land cover is classified. The result of 94.75% was obtained when classifying the land cover only into five classes. However, a very interesting accuracy of 92.37% was achieved by the model when trained to classify eight classes. These results are superior to those reported in the related bibliography.