## 1 Introduction  
When a natural disaster such as an earthquake or tsunami occurs, visual information can provide essential data for emergency management. Aerial images obtained from satellites, aircraft, and drones can simultaneously capture a wide range of features; thus, they may be utilized for response and recovery operations after a disaster event [1, 2]. The higher the shooting altitude, the wider the view; however, the visibility of optical images can deteriorate owing to the lack of a light source or the influence of clouds. Alternatively, synthetic aperture radars (SARs) can capture data over a large area without much deterioration. Therefore, SARs are often used in disaster situations, such as damage area detection [3, 4, 5], infrastructure damage assessment [6, 7], etc. However, SAR has some problems in practical use, including (a) they are relatively less readable by humans and (b) landmark points may be difficult to locate, especially with coarse resolution data. To obtain geographic information, the combined use of SAR images and optical data can provide a wealth of information. Thus, the geometric registration of the two data is essential, which may be challenging. The accurate registration of SAR images and optical data can provide valuable information, which would otherwise be difficult, especially during natural disasters, such as floods of large magnitude. Hence, the precise registration of optical and microwave data can support rapid scanning of flood areas or the identification of collapsed buildings after an earthquake or tsunami for rescue and evacuation efforts. The pixel values in SAR images represent the intensity of electromagnetic waves, and they are expressed in backscatter values. This differs from visible light, thereby resulting in varying responses of several ground features. Thus, when handling SAR images, it is common to compensate for their low readability by registering them with optical images. Image registration is traditionally performed using a digital elevation model (DEM) [8]. However, when the spatial resolution of both the optical and SAR images is high, DEM may not be effective. Moreover, when a disaster event occurs, landforms may be drastically changed such that DEM may not achieve accurate image registration. An alternative method is image-based registration, which is independent of DEM. In this method, complementing factors associated with the acquisition and processing of optical and SAR data are considered because optical images are prone to atmospheric disturbances, especially during disasters such as floods, while SAR can acquire data in any weather. Combining the SAR data acquired during the disaster and the optical images before/after the disaster can make response and recovery operations much easier. Meanwhile, an automated method for the geometric rectification of images from different sensors is not as easy due to variations in the spectral response of ground features. Hence, two datasets can be combined by translating the appearance of a SAR image to an optical image using generative adversarial networks (GANs) [9] for subsequent keypoint matching. We proposed a method for finding local feature correspondence between multimodal (SAR and optical) images using an image-based feature keypoint extraction, description, and matching algorithm [10], as shown in Figure 1. Image translation with a GAN is used to transform a SAR image into an optical image. Although this method has good accuracy, the blurring of features could be a significant issue (Figure 2). To obtain more corresponding points, blurring should be removed to highlight the local features. However, it is difficult to achieve this with conventional GANs. Figure 1: Outline of the proposed method. Using a GAN as a pre-processing step before keypoint matching, local correspondence was established for multimodal image registration. In this work we proposed a SAR and optical image registration method for overcoming the above-mentioned problem.