Cloud Removal Using Sparse Dark Channel Region Detection: A Systemic Literature Review

Remote sensing satellite technology has revolutionized the way we gather information about our planet. Through the use of advanced imaging capabilities, satellite images have become invaluable in various aspects of daily life. These images are extensively utilized in environmental protection, agricultural engineering, and other fields. Remote sensing satellite maps are used for tasks such as geological mapping, monitoring urban heat islands, environmental surveillance, and detecting forest fires from remote sensing images. However, clouds present a significant hindrance when utilizing satellite imagery for ground observations, as they obstruct the view and can limit the accuracy of the analysis. While there are numerous advanced state-of-the-art approaches available, it is important to note that they often require a substantial amount of data for training. On the other hand, if a more general approach is desired without the need for extensive training data, pixel-based methods provide a viable option. One of the widely used pixel-based methods for cloud removal in satellite images is Dark Channel Prior (DCP). DCP is often combined with other methods to improve the image quality. This systematic literature review will demonstrate the development of the DCP method in cloud removal from satellite images. Keywords: Cloud removal, dark channel prior (DCP), satellite imagery, remote sensing