Using Adversarial Network for Multiple Change Detection in Bitemporal Remote Sensing Imagery

Change detection by comparing two bitemporal images is one of the most challenging tasks in remote sensing. At present, most related studies focus on change area detection while neglecting multiple change type identification. In this letter, an attention gates generative adversarial adaptation network (AG-GAAN) is proposed on multiple change detection. The AG-GAAN has the following contributions: 1) this method can automatically detect multiple changes; 2) it includes attention gates mechanism for spatial constraint and accelerates change area identification with finer contours; and 3) the domain similarity loss is introduced to improve the discriminability of the model so that the model can map out real changes more accurately. To demonstrate the robustness of this approach, we used the Google Earth data sets that include seasonal variations for change detection and understanding. The experimental results demonstrated that the proposed method can accurately detect the multiple change types from bitemporal imagery.