*5.2. Limitation and Future Work*

Although the proposed method efficiently extracts inconsistent parcels by an automatic process, its performance depends on the acquisition time of the hyperspectral UAV image, which is input to the discrepancy analysis. For example, some crop parcels are erroneously classified as bare soil because the vegetation vitality is low when the image is captured. The results of the discrepancy analysis may depend on the matching criteria of the land category items and land cover classes, because no absolute rules for defining inconsistency can be established. In conclusion, changing queries for the comparison must be preceded according to the mapping criteria. On the plus side, the proposed method is generalizable to various cadastral systems through flexible modification of the matching criteria between the land categories and land coverage.

In a future, we will develop a classification network that distinguishes finer classes in hyperspectral UAV images with higher classification accuracy. For example, crop land can be divided into rice fields and other fields for finding complex matching relationships between land use and the land categories of cadastral maps.
