Abstract: Surface change detection is an important component of the applications of remote sensing big data. However, it is essentially subject to manual interactive interpretation in actual production. With this regard, this paper developed an application method and software for the automatic detection of changes in vegetation regions on a polygon scale using correlation coefficients and feature analysis. The details are as follows. Correlation coefficients of surface features were constructed using spectral and textural features, and then the changes in vegetation regions were detected using the similarity measurement method. According to the analysis of spectral differences between the vegetation and other types of surface features, the red band ratio was selected to remove spurious changes. Finally, the change detection software was designed and developed using the.NET framework and the ArcGIS Engine component library for secondary development. Test data were imported into the software for change detection. The test results show the accuracy rate and omission rate of the software in the change detection were 94.3% and 8.5%, respectively. Furthermore, the software has a higher automatic level compared to manual interactive interpretation. In conclusion, the method and software developed in this study can be widely applied.