Change detection in Tai’an city of eastern China using a pair of qual-polarimetric Advanced Land Observing Satellite phased array type L-band synthetic aperture radar (ALOS PALSAR) data was studied. The procedures consisted of polarimetric features extraction, optimal polarimetric feature group selection, supervised classification, and result accuracy assessment. Feature extraction from PALSAR data was performed first, and then the polarimetric features were categorized into several groups. Polarimetric optimum index factor (POIF) and distance factor (DF) were selected to measure and evaluate the suitability of each feature group for urban change detection. The best group of features was identified including linear polarization correlation coefficient (ρ HH–VV ), right–left (R–L) circular polarization correlation coefficient (ρ RR–LL ), total power (TP), and cross-polarization isolation (XPI). Afterward, four difference images of the identified features extracted from the two PALSAR data were derived, respectively. Then, the random forest (RF) classifier was employed to perform a supervised classification of the four difference images. Three classes were quantified, including no-change, change from undeveloped area to developed area, and vice versa. The overall accuracy of change detection was about 84% and Cohen’s Kappa coefficient was 0.71. Consequently, satisfactory outcomes were obtained in the application of the polarimetric ALOS PALSAR data of moderate resolution in detecting urban land use and land cover type changes.