A Post-Classification Comparison Method for SAR and Optical Images Change Detection

This letter proposes a method for the change detection in multisensor remote sensing images. The proposed method combines multitemporal segmentation and compound classification. In consideration of the particularity of multisensor images, multitemporal segmentation is applied to generate homogeneous objects. This process can reduce the salt and pepper effect that is inevitable in pixel-based methods and reduce the false alarms caused by area transitions and object misalignment in traditional object-based methods. Then, compound classification is carried out at the object level. This process exploits temporal correlations and overcomes the error propagation of traditional postclassification comparison methods. The change map is generated by comparing the classification maps at different times. Experimental validation is conducted with GaoFen3, Terrasar, GaoFen2, and Google Earth data.