Comparison of two data fusion approaches for land use classification
This work addresses the need for accurate land use maps for land management and planning, but it is incremental as it compares existing fusion methods without introducing new techniques.
The study tackled the problem of land use classification by comparing pre-classification and post-classification data fusion approaches, finding that pre-classification fusion achieved the best results with 97% overall accuracy and an 88% macro-mean F1 score.
Accurate land use maps, describing the territory from an anthropic utilisation point of view, are useful tools for land management and planning. To produce them, the use of optical images alone remains limited. It is therefore necessary to make use of several heterogeneous sources, each carrying complementary or contradictory information due to their imperfections or their different specifications. This study compares two different approaches i.e. a pre-classification and a post-classification fusion approach for combining several sources of spatial data in the context of land use classification. The approaches are applied on authoritative land use data located in the Gers department in the southwest of France. Pre-classification fusion, while not explicitly modeling imperfections, has the best final results, reaching an overall accuracy of 97% and a macro-mean F1 score of 88%.