Traditional remote sensing change-detection algorithms only generate change-detection map and few quantitative evaluation indicators as the results, but they are unable to provide comprehensive analysis and further understanding of the detected changes. Aiming to assess regional development, we develop a comprehensive analysis method for human-driven environmental change by multitemporal remote sensing images. In order to adapt to analyze the time-varying multiple changed objects, an observed object-specified dynamic Bayesian network (i.e., OOS-DBN) is first proposed to adjust DBN structure and variables. Using semantic analysis for the relationship between multiple changed objects and regional development, all levels of situations and evidences (i.e., detected attributes of changed objects) are extracted as hidden variables and observed variables and inputted to OOS-DBN. Furthermore, conditional probabilities are computed by levels and time slices in OOS-DBN, resulting in the comprehensive analysis results. The experiments on the coastal region in Huludao, China, from 2003 to 2014 demonstrate that comprehensive analysis of changes reflecting that reclamation, construction of infrastructure, and New Huludao port contributed to the regional development. During four time slices, this region experienced rapid and medium-speed development, whose corresponding probabilities are 0.90, 0.87, 0.41, and 0.54, respectively, which is consistent with our field surveys.