## 1 Introduction  
Aerial imagery has been widely used for monitoring the surrounding environment due to its long history. Orthoimages created from aerial images can provide high-quality geospatial information taken at lower altitudes than satellite images. Continuously monitoring a rapidly changing environment requires reducing the observation period for a site. However, the tradeoff between spatial resolution and ground coverage prevents aerial images from covering a wide area. The role of aerial imagery has been gradually replaced by satellite imagery with its wide area coverage and regular repeat pass capabilities. Moreover, satellites equipped with multispectral sensors have enabled multiple applications such as resource management, urban research, facility mapping, and disaster monitoring. The resolution of most of the current satellite images is still lower than that of aerial images. The price of commercially available high-resolution (HR) satellites has frequently hindered many researchers' progress in their projects. In most countries, including Korea, HR aerial orthoimages are provided to the public for free [1]. Furthermore, in the United States and the European Union, low- and medium-resolution satellite images are provided free of charge to users around the world. Research is needed to increase the resolution of mid- and low-resolution satellite images using freely available HR aerial images. In the field of remote sensing, a visible improvement of image resolution primarily implies pan-sharpening. This method improves the resolution of low-resolution multispectral images using an HR panchromatic image. There are two typical approaches, one using Intensity-Hue-Saturation (IHS) information [2] and one using principal component analysis (PCA) [3]. The primary concern for pan-sharpening is that it is applicable only when an HRpanchromatic image is available. Consequently, the resolution of the pan-sharpened image cannot be higher than that of the input panchromatic image. With the recent development of deep learning techniques, studies to produce images with higher resolution than the input image have been conducted. Several studies using deep learning techniques have been published in the remote sensing community. Related studies can be largely divided into two usage categories: multiple sensors from one platform and multiple sensors from multiple platforms [4; 5; 6; 7; 8; 9; 10]. Improving the resolution of multispectral sensors from one (same) platform is usually performed by merging lower and higher multispectral images. [PERSON] et al. [5] enhanced a 20 m shortwave infrared (SWIR) image acquired by Sentinel-2 into a 10 m SWIR image. Similar to the pan-sharpening approach, the four-channel 10 m visible and NIR resolution images of Sentinel-2 were regarded as panchromatic. A shallow convolutional neural network (CNN) was constructed to improve the resolution of the SWIR image. The limitation of this study is that only the resolution of an SWIR image can be improved. [PERSON] et al. [6] presented research results that can address this limitation. By constructing deep and dense neural network models, DSen2 and VDSen2, they improved the 20 m resolution of three red-edge and three SWIR images, two 60 m resolution images of water vapor, and 60 m SWIR images of Sentinel-2 images into 10 m. They asserted that the model could be extended and improved from 20 m and 60 m to a 10 m resolution. However, the first category cannot produce images with higher resolution than the maximum resolution provided by the platform. Another category is improving the resolution of multispectral sensors from multiple (different) platforms. Few studies have improved 30 m Landsat-8 satellite images to 10 m using Sentinel-2 images. [PERSON] et al. [7] proposed the extended super-resolution convolutional neural network (ESRCNN) by blending Landsat-8 and Sentinel-2 data. They demonstrated the effectiveness of the deep learning-based fusion method for improving the resolution of Landsat-8 imagery.