Creation of a Deep Learning Model for the Enhancement and Reconstruction of Environmental Images

Now, the emergence of image-based data, remote sensing (which can practically guide us to collect better image data) turned into one of the best supporters of environmental monitoring. These techniques offer an effective way to monitor wide-area environmental events, like deforestation, climate change, and urban expansion. The key benefit of such techniques is the collection of data in near real-time over extensive areas, allowing scientists and managers to keep track of operational environments quickly and accurately. Nevertheless, noise, low-resolution, and distortion from the atmospheric condition or sensors would cause problems to the raw data that are collected from these sources (Wang et al., 2022). Heap over the above circumstances, the extraction of useful information from environmental images needs to be enhanced and reconstructed, especially in the field of high-resolution, such as land-use mapping and disaster monitoring.