Abstract
This study focused on land cover mapping based on synthetic images, especially using the method of spatial and temporal classification as well as the accuracy validation of their results. Our experimental results indicate that the accuracy of land cover map based on synthetic imagery and actual observation has a similar standard compared with actual land cover survey data. These findings facilitate land cover mapping with synthetic data in the area where actual observation is missing. Furthermore, in order to improve the quality of the land cover mapping, this research employed the spatial and temporal Markov random field classification approach. Test results show that overall mapping accuracy can be increased by approximately 5% after applying spatial and temporal classification. This finding contributes towards the achievement of higher quality land cover mapping of areas with missing data by using spatial and temporal information.
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