Automatic Cloud Detection and Removal in Satellite Imagery Using Deep Learning Techniques

With the rapid advancement of remote sensing technology, satellite imagery has become increasingly vital in global geographic information systems, environmental monitoring, and resource management. However, cloud cover frequently degrades the quality of satellite images, limiting their effectiveness in many critical areas. Traditional methods for cloud detection and removal, such as threshold analysis and spectral feature analysis, often fail to achieve satisfactory results due to environmental constraints and algorithmic limitations. In response, this study employs deep learning techniques, specifically superpixel segmentation and generative adversarial networks (GAN), to address this issue. This paper begins by discussing the importance of cloud detection and removal in satellite imagery and reviews existing major techniques and methods. It then explores the application of superpixel segmentation based on local adaptive distance for automatic cloud boundary identification, along with innovative applications of GAN for surface information reconstruction in cloudcovered areas. These methods not only enhance the accuracy of cloud detection but also effectively optimize the cloud removal process, paving the way for further applications of satellite imagery.