Novel Enhanced UNet for Change Detection Using Multimodal Remote Sensing Image

Land cover change detection (LCCD) with bitemporal remote sensing images has been widely used in practical applications. However, when the bitemporal images are multimodal remote sensing images (MRSIs) which are acquired with different sensors, the change detection performance may be unsatisfactory, because MRSIs cannot be compared directly to generate a change magnitude and obtain a change detection map. Here a novel approach is proposed to overcome this problem, i.e., the Enhanced UNet (E-UNet) which learns deep shared features from MRSIs to achieve change detection with MRSIs. First, a pre-event image to post-event image (P2P) transformation module based on classical Cycle-consistent Generative Adversarial Network (CGAN) is suggested to embed at the head of the proposed E-UNet to translate the pre-event image to a post-event image one. Then, multi-scale convolutions are added at each encoding layer to capture the various shapes and sizes of ground targets. Finally, a Polarized Self-Attention (PSA) module is employed before beginning the decoding progress of E-UNet with an aim to pay extra attention to changed areas. Compared with five typical state-of-the-art methods, experimental results based on two pairs of MRSIs well demonstrated the feasibility and advantages of the proposed E-UNet for LCCD with MRSIs in terms of visual observations and quantitative evaluations. For example, the improvement is 4.19% and 4.75% in terms of the overall accuracy for the Sardinia dataset and California dataset, respectively. The code of the proposed approach can be found at https://github.com/ImgSciGroup/E-UNet.