A fully polarimetric synthetic aperture radar (PolSAR) image allows the generation of a number of polarimetric descriptors. These descriptors are sensitive to changes in land use and cover. Thus, the objective of this study is twofold: first, to identify the most effective descriptors for each change type and ascertain the best complementary pairs from the selected polarimetric descriptors; and second, to develop an information fusion approach to use the unique features found in each polarimetric descriptor to obtain a better change map for urban and suburban environments. The effectiveness of each descriptor was assessed through statistical analysis of the sensitivity index in selected areas and through change detection results obtained by using the supervised thresholding method. A good agreement was found between the statistical analysis and the performance of each descriptor. Finally, a polarimetric information fusion method based on the coupling of modified thresholding with a region-growing algorithm was implemented for the identified complementary descriptor pairs. The mapping accuracy, as measured by the Kappa coefficient, was improved by 0.09 (from 0.76 to 0.85) with a significant reduction of false and missing alarm rates compared to using single PolSAR images.