Enhancing Land Cover Mapping: A Novel Automatic Approach to Improve Mixed Spectral Pixel Classification

The increasing availability of high-resolution, open-access satellite data facilitates the production of global Land Cover (LC) maps, an essential source of information for managing and monitoring natural and human-induced processes. However, the accuracy of the obtained LC maps can be affected by the discrepancy between the spatial resolution of the satellite images and the extent of the LC present in the scene. Indeed, several pixels may be misclassified because of their mixed spectral signatures, i.e., more than two LC classes are present in the pixel. To solve this problem, this paper proposes an approach that explores the possibility of using simple but effective unmixing approaches to enhance the classification accuracy of the mixed spectral pixels. The results showed that several pixels, including buildings and grassland LC, are typically classified as cropland. By unmixing their spectral content, it is possible to extract the most prevalent class within the area of each pixel to update the classification map, thus sharply increasing the map accuracy. These promising preliminary results indicate the potential for broader applicability and efficiency in global LC mapping.