Identifying source objects in astronomical observations, in particular with reliable algorithms, is extremely important in large-area surveys. It is of great importance for any source detection algorithm to limit the number of false detections since follow up investigations are timely and costly. In this paper, we consider two new statistical procedures to control the false discovery rate (FDR) for group-dependent data-the two-stage BH method and adaptive two-stage BH method. Motivated by the belief that the spatial dependencies among the hypotheses occur more locally than globally, these procedures test hypotheses in groups that incorporate the local, unknown dependencies. If a group is found significant, further investigation is done to the individual hypotheses within that group. Importantly, these methodologies make no dependence assumption for hypotheses within each group. The properties of the two procedures are examined through simulation studies as well as astronomical source detection data.