| Title |
Automatic Generation of Land Cover Map Using Residual U-Net |
| Authors |
유수홍(Yoo, Su Hong) ; 이지상(Lee, Ji Sang) ; 배준수(Bae, Jun Su) ; 손홍규(Sohn, Hong Gyoo) |
| DOI |
https://doi.org/10.12652/Ksce.2020.40.5.0535 |
| Keywords |
항공정사영상;토지피복지도 자동 제작 Aerial ortho photo;Landsat 8;Residual U-Net;Automatic land cover map generation |
| Abstract |
Land cover maps are derived from satellite and aerial images by the Ministry of Environment for the entire Korea since 1998. Even with their wide application in many sectors, their usage in research community is limited. The main reason for this is the map compilation cycle varies too much over the different regions. The situation requires us a new and quicker methodology for generating land cover maps. This study was conducted to automatically generate land cover map using aerial ortho-images and Landsat 8 satellite images. The input aerial and Landsat 8 image data were trained by Residual U-Net, one of the deep learning-based segmentation techniques. Study was carried out by dividing three groups. First and second group include part of level-II (medium) categories and third uses group level-III (large) classification category defined in land cover map. In the first group, the results using all 7 classes showed 86.6 % of classification accuracy The other two groups, which include level-II class, showed 71 % of classification accuracy. Based on the results of the study, the deep learning-based research for generating automatic level-III classification was presented. |