Area Coverage Planning Algorithm Based on Discrete Points Access Information Clustering

Admittedly, area coverage constitutes a crucial aspect of satellite remote sensing. However, the traditional strip-coverage approach, which generally involves sequentially laying out parallel imaging strips over the target area based on fixed satellite orbital paths and nadir-pointing observations [Ref], grapples with inefficiency and limited adaptability. By discretizing the target area based on the minimum observation swath width of all satellites involved, this study proposes an area coverage planning algorithm based on discrete points access information clustering (AICPDA), enabling maximum coverage of the target area within the shortest possible time. It simultaneously circumvents the complexity arising from variations in satellite observation swath widths and orbital orientations. The algorithm demonstrates robust adaptability to diverse area characteristics and satellite types, alongside excellent scalability. Consequently, it effectively tackles the challenge of maximizing coverage of a designated area by multiple remote sensing satellites within a specified timeframe in the realm of satellite remote sensing task planning.