In the 1960s, the US launched a series of satellite projects aimed at building a reconnaissance system. The CORONA project is in operation between 1960 and 1972. During the operation, a large number of satellite images are taken. These generated images are called CORONA images. Until 1995, these images remained classified. Numerous researchers have exploited CORONA images and performed studies on changes on the earth’s surface. This study adapts the existing DeOldify model, which is dedicated to colourizing images to specifically colourize CORONA satellite images through retraining the model on the CORONA image dataset. The CORONA image dataset consists of greyscale CORONA images and colour reference images covering the same region and
having little or no change in the image content. A generative adversarial network is used for building this model. A U-Net-based generator network and a binary-classifierbased critic network train in an alternative approach. To improve the training efficiency, transfer learning and NoGAN training techniques are implemented. The generated images are evaluated both quantitatively, using RMSE and PSNR, and qualitatively, using a user study asking the participant if the displayed colour image looks natural. Results show that generated images with good performance on RMSE and PSNR do not necessarily have plausible colours. Compared with the original DeOldify model, the retained model can produce images with more natural and plausible colours, achieving a result of 70.1% of the participants thinking they are real, although these images have
poorer reconstructing quality. To visualize the results, a web mapping application is developed with Geoserver and Leaflet. The server stores data and publish it through WMTS or WFS. Cache service and image pyramid are used to accelerate the response on the client side. The web map client provides an interactive map interface for the users to control the displayed layers and download the selected layer in a self-defined ROI through WPS.