## 1 Introduction  
Monitoring distribution and changes in a region of interest (RoI) is a fundamental task in land-cover classification and has been part of many applications such as urban management [1], land-used management [2], and crop classification [3]. Land-cover classification generates information on the status of land use. It has been used especially in the South Korea highlands for a study of classifying cabbage as well as potatoes [4]. The reason for identifying cabbage in the highlands is that these regions, despite their short growing seasons, are the major cultivation areas for these cabbage. Therefore, it is important to develop land-cover classification methods to investigate the agricultural highlands of South Korea. Traditionally, manual field surveys have been used to identify agricultural lands and derive land maps [4]. However, this method requires a significant amount of time and manpower. To replace manual field surveys, many studies have been conducted with remote sensing (RS) imagery using satellites and aircrafts [5]. However, this approach is disadvantageous in that they are mostly low-resolution images and can be degraded by weather conditions or shadows that complicate the collection of accurate information [6; 7]. Especially, these issues have been a major problem for the highlands of South Korea [4]. Recently, the development of RS technology has made capable high-resolution photography with unmanned aerial vehicles (UAV) [8] has created a trend away from satellite and aircraft imagery. UAV imagery is less likely to be affected by weather because it is taken at lower altitudes with higher spatial and spectral resolution capabilities that improve accuracy in rendering ROI [8]. In this study, we operated UAVs in the highlands and collected high-resolution photographs taken under different spatiotemporal conditions to generate information on cabbage cultivation fields. In general, a multispectral sensor has been equipped with satellites, aircrafts, and UAVs to generate information on RoI [9]. Multispectral sensors capture not only visible wavelength reflectances but also infrared wavelengths, which enable the monitoring of vegetation growth [10; 11]. Based on theoretical backgrounds for agriculture, we equipped UAVs with a multispectral sensor and collected various information on cabbage cultivation fields in South Korea highlands. In addition, we analyze the effect on infrared wavelengths in terms of performance on detecting cabbage fields at a pixel-level. Recently, convolutional neural networks (CNN) have led to advances for computer vision tasks such as image classification [12], object detection [13], and semantic segmentation [14]. In particular, the semantic segmentation task aims to assign a class label to each pixel in an image [14]. Accordingly, CNN-based semantic segmentation algorithms have also been successfully applied to analyzing UAV imagery. For instance, semantic segmentation algorithms were applied to UAV images to ascertain the ratio of roads in cities and to assess pavement crack segmentation on an airport runway [15; 16]. In this study, we propose a semantic segmentation framework based on UAV imagery to automate the process of identifying the cabbage fields in the South Korea highlands. First of all, UAV multispectral images of cabbage fields collected under various conditions were annotated with the help of agricultural experts to generate information on the important cabbage. Second, we trained several different semantic segmentation models and compared their performances. Moreover, we made extensive studies to analyze the generalization performance of our models. Finally, we analyzed the effects of infrared wavelengths on identifying cabbage fields at a pixel-level.