Aerial images are an outstanding option for observing terrain with their high-resolution (HR) capability. The high operational cost of aerial images makes it difficult to acquire periodic observation of the region of interest. Satellite imagery is an alternative for the problem, but low-resolution is an obstacle. In this study, we proposed a context-based approach to simulate the 10 m resolution of Sentinel-2 imagery to produce 2.5 and 5.0 m prediction images using the aerial orthoimage acquired over the same period. The proposed model was compared with an enhanced deep super-resolution network (EDSR), which has excellent performance among the existing super-resolution (SR) deep learning algorithms, using the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root-mean-squared error (RMSE). Our context-based ResU-Net outperformed the EDSR in all three metrics. The inclusion of the 60 m resolution of Sentinel-2 imagery performs better through fine-tuning. When 60 m images were included, RMSE decreased, and PSNR and SSIM increased. The result also validated that the denser the neural network, the higher the quality. Moreover, the accuracy is much higher when both denser feature dimensions and the 60 m images were used.
<p><strong>Abstract.</strong> As the recent disaster is so difficult to predict when and where it would hit, so it requires paradigm for disaster shifts from response to preparedness. In order to respond this change, NDMI has studied disaster scientific investigation (DSI) technologies for revealing systematically the root cause of disaster and protecting repetitive recurrence.<br> The purpose of this study is to propose a convergence approach between data acquired from different types of sensors on a van-type investigation platform and UAVs of NDMI and assess their applicability for timely natural and man-made disaster mapping and monitoring. In order to evaluate its applicability for rapid disaster mapping, we pre-tested the proposed approach for NDMI site in Ulsan, Korea. For the enhancement of the direct geo-referencing accuracy of UAV imagery captured from on-board camera of DJI and the creation of more accurate map products, camera IOPs refinement and bundle adjustment were also performed with minimal GCPs. Finally, we conducted UAV data registration with LiDAR point clould for disaster mapping applications.</p>
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