How to improve 3-m resolution land cover mapping from imperfect 10-m resolution land cover mapping product?

<p>Land cover mapping has made drastic progress with the improvement of the resolution of remote sensing images in recent research. However, with various limitations of public land cover datasets, human efforts on interpreting and labelling images still account for a significant part of the total cost. For example, it took 10 months and $1.3 million to label about 160,000 square kilometers in the Chesapeake Bay watershed in the northeastern United States. Therefore, it is significant to consider the human interpreting cost of the large-scale land cover mapping.</p><p> </p><p>In this work, we explore a possible solution to achieve 3-m resolution land cover mapping without any human interpretation. This is made possible thanks to a 10-m resolution global land cover map developed for the year of 2017. We propose a complete workflow and a novel deep learning based network to transform the imperfect 10-m resolution land cover map to a preferable 3-m resolution land cover map, which is beneficial to reduce the research thresholds in this community and give similar studies as an example. As we use the imperfect training label, a well-designed and robust approach is strongly needed. We integrate a deep high-resolution network with instance normalization, adaptive histogram equalization, and a pruning process for large-scale land cover mapping.</p><p> </p><p>Our proposed approach achieves the overall accuracy (OA) of 86.83% on the test data set for China, improving the previous state-of-the-art accuracies of 10-m resolution land cover mapping product by 5.35% in OA. Moreover, we present detailed results obtained over three mega cities in China as example and demonstrate the effectiveness of our proposed approach for 3-m resolution large-scale land cover mapping.</p>