Candidate-Aware and Change-Guided Learning for Remote Sensing Change Detection

Change detection (CD) in remote sensing images aims at revealing Earth surface changes between co-registered bitemporal images. A common way to reveal changed areas is to directly mix bitemporal features and generate CD results through supervised learning. However, a certain change usually corresponds to a real object in either of the two images, which exhibits coarse/fine shapes in different scales. Therefore, a coarser-to-finer method called candidate-aware and change-guided network (CACG-Net) is proposed to effectively detect changes, where candidate objects are revealed and associated with interesting changes. Specifically, there are three key components. They are multistage change decoder (MCD), candidate-aware learning (CAL), and change guidance module (CGM). MCD reveals the most important changed objects in the coarse shape from the basic features extracted by the backbone (ResNet-18). To capture changes of interest, CAL is designed to select candidate objects in each temporal image, where a segmenter is utilized with variant change-losses. CGM intends to enrich the change details step-by-step by combining coarser change results and finer features so that changed objects are gradually revealed in a coarser-to-finer way. Furthermore, deep supervision is employed throughout the layers of CACG-Net in the training procedure, which mitigates the learning difficulty in both deep and shallow layers. Test results on four popular datasets indicate that the proposed method outperforms several state-of-the-art CD algorithms in terms of accuracy and efficiency.