Automatic Land Use and Land Cover Classification by Means of Characterising Colours

Land Use Land Cover (LULC) classification plays a crucial role in the optimisation of agricultural lands to cope with global warming and increase crops. Many existing methodologies for LULC classification rely on machine learning algorithms and/or hyperspectral imaging, both of which necessitate extensive annotated datasets and substantial computational resources. Moreover, these methods could yield results that are in some cases inaccurate or lack interpretability. This paper introduces an innovative and effective approach to extract characterising colours from different land types. The proposed solution manages to automatically generate the sets of colours corresponding to categories of land, from a small set of annotated images, in just one iteration. Such colours were then used to classify lands in other images by analysing colour distributions and assessing pixel densities. The results demonstrated the precision and accuracy of our approach, which successfully classifies up to three categories of land across multiple images.