Supervised Change Detection Using Prechange Optical-SAR and Postchange SAR Data

Change detection using satellite/aerial images is used to quantify the impacts of many natural and man-made disasters. At the occurrence of such events, both prechange optical and synthetic aperture radar (SAR) images can be obtained by going back in time. However, the availability of the postchange optical image is often hindered by the presence of artifacts like clouds. To circumnavigate this, we propose a novel change detection data setting that uses both optical and SAR images prechange, yet only SAR imagery postchange. For this challenging scenario, we propose a Siamese network that processes the prechange and postchange SAR inputs using a shared set of weights, while the prechange optical input is processed using a network that do not share the weights with the SAR inputs. The encoded weights from the three networks are fused and finally decoded using a common decoder to obtain the change map. Our model effectively fuses multisensor information and can obtain satisfactory result despite the absence of the postchange optical image. Experimental results on a multisensor urban dataset demonstrate the effectiveness of the proposed approach.