Land cover updates are key datasets in geographic national conditions monitoring, environmental change assessment, and ecological system protection. Remote sensing technology has become an important tool for an update of land cover datasets. However, as the complexity of spectrum, texture and temporal characteristics, omission and commission errors usually occur in land cover data, leading to the issue of spatiotemporal land cover object inconsistency. Currently, detection of land cover data inconsistency gives priory to manual inspection, and partial automation is implemented. In practice, a huge of workmen and time are needed in data inconsistency detection, lacking of an automated detection tool. The challenge of land cover data inconsistency detection in raster space is analyzed in this study. A logic quantifiers-based topological relationships calculation in raster space, the initial-judgment rules construction based on confidence interval, and a post-judgement using spatial multi-matching are proposed, forming a “relation-rule-judgment” detection system. Inconsistency detection of the GlobeLand 30 datasets is conducted in study areas of Linqu and Kenli, Shandong, China. Comparing with statistics inconsistency detection, and taking remote sensing images as reference, the effectiveness of inconsistent land cover objects detected in update is validated, and this method is proved to be practically feasible.
Acta Geodaetica et Cartographica Sinica