Land cover classification using Machine Learning Techniques

The project intends to perform and compare different supervised classification methods, using a SENTINEL-2A satellite image to settle on some separable land cover classes within Lunds Kommun, and create a land cover map of the area, by exploring different objects’ reflectance properties, and compare their accuracy.

Four ML techniques were tested to create a Land cover classification of Lund Municipality (Lunds Kommun):


The first three ones were done using R, and the last one was made in IDRISI TerrSet.


Fieldwork was carried out in order to collect training and evaluation data - 299 GPS points were collected and identified among 6 land cover classes in the field, 80% was used for training and 20% for classification validation.

Based on the visual interpretation of the true and false image composites, the spectral signature of the objects, and the fieldwork, the classes defined were: 

Urban Areas

Agriculture Lands

Water

Coniferous Forest

Deciduous Forest

Bare Soil

Among the methods, the MLP showed the best accuracy and Kappa results.