Enhancement of Low-Resolution Remote Sensing Images Using ANN

Remote-sensing images particularly crucial in wide geographic visual analysis, including change in climate, 3D modelling, and earth’s global observation. As a result of the bad effects of satellite data technology, the transparency and comprehensibility of remote-sensing images are greatly impacted by weather conditions including fog, snowstorms, hurricanes, clouds, and so forth. Many real-world tasks, such surveillance for night time tracking and marine disasters, are made particularly difficult by minimal photography. Remote sensing photos may benefit from improved interpretation and visualization effects by having their brightness increased using Image Enhancement (IE) technology. Picture enhancement, picture super resolution, and other image-processing applications have found significant success with artificial neural networks (ANN). The results of image enhancement algorithms such as CNN and ANN are compared and investigated. The approach outcomes also present aesthetic advantages in the sense of noise removal and retaining the integrity of colors and textures.