This study aims to explore cost-effective strategies for efficiently dismantling global complex networks into mutually isolated connected components, particularly in the context of a space information network (SIN). We introduce a novel metric called the coverage centrality, which comprehensively considers the centrality of nodes and links within a satellite coverage area, which integrates the topological and geographical structural information about the network. Through numerical experiments on real-world networks, we demonstrate that the proposed indicator significantly outperforms traditional methods in terms of both effectiveness and efficiency, which tends to select nodes close to the average betweenness of the network for removal rather than solely focusing on nodes with higher centrality, providing a new perspective for us to deeply understand the disintegration behavior of complex networks. The current work not only offers potential application in areas such as anti-terrorist measures and epidemic prevention or control, but also provides new tools to analyze the characteristics of SIN models.