Abstract
This paper describes our approach in the land cover classification with low- and high-resolution labels challenge of 2020 IEEE Data Fusion Contest. The challenge features the large-scale land cover mapping based on weakly annotated samples. We firstly refine the samples based on prior knowledge on class confusion and the confidence of the samples. Subsequently, an ensemble of random forests is used for classification and a post-processing step is performed to improve the results. This approach achieves an average accuracy of 0.6142 on the test dataset and achieves the 1st place in Track 2 of the Data Fusion Contest 2020.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
