## 6 Conclusions  
In this study, we have proposed a semantic segmentation framework based on UAV images to automate the process of identifying cabbage fields in the highlands of South Korea. First, we collected high-resolution multispectral images by operating UAVs over different highlands in South Korea to generate information on the important cabbage. Second, we applied ECC transformation to handle misalignment between channels and generated pixel-wise ground truth labels. We compared the performances of detecting cabbage cultivation fields by three semantic segmentation models and four combinations of input wavelengths and concluded that DeepLab V3+, which was trained on RGB and  
Figure 10: Reflection histograms of the each channel. Each row contains histograms for a single wavelength. Each column corresponds to a dataset. Green and gray distributions correspond to the distribution of cabbage-pixel reflection and other-pixel reflection, respectively. RE wavelengths, performed the best. We demonstrated that the model was effective in distinguishing between the cabbage fields, fields of weeds, and buildings despite changes in operational dates and regions. Based on the results of our proposed framework, we expect agricultural officials to save time and manpower when collecting information about cabbage cultivating fields in South Korea highlands by replacing manual field surveys. In future studies, we plan to apply semantic segmentation models to detect multiple crops such as cabbage, peppers, and beans. We also expect to make use of additional infrared wavelengths, such as RE and NIR, by incorporating several CNN-based encoders to learn low-level representations from each wavelength and enhance model performance. Conceptualization, S.B., S.H. and S.K. ; methodology, Y.J., S.L., Y.L., H.K. and S.P. ; investigation, Y.J., S.L., Y.L., H.K., S.P., M.K., S.H. and S.K. ; data curation, S.B. and M.K. ; writing--original draft preparation, Y.J., Y.L. and H.K. ; writing--review and editing, S.H. and S.K. ; supervision, S.K. All authors have read and agreed to the published version of the manuscript. This research was supported by a grant from the Korea Land and Geospatial Informatix Corporation Spatial Information Research Institute (LX SIRI), the Brain Korea 21 FOUR, Ministry of Science and ICT (MSIT) in Korea under the ITRC support program (IITP-2020-0-01749) supervised by the IITP, the National Research Foundation of Korea grant funded by the MSIT (NRF-2019 RIA4A1024732), and the Ministry of Culture, Sports and Tourism and Korea Creative Content Agency (R2019020067). Not applicable. The data presented in this study are not publicly available due to privacy and legal restrictions. The authors would like to thank the Editor and reviewers for their useful comments and suggestions, which were greatly helpful in improving the quality of the paper. The authors declare no conflict of interest. References  
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