MFGAN: multi feature guided aggregation network for remote sensing image

Remote sensing image change detection is an important technology in remote sensing data analysis. Existing mainstream solutions are divided into supervised and unsupervised solutions. Among supervised methods, most remote sensing image change methods based on deep learning are related to semantic segmentation. However, these methods only use deep learning model to extract and process single-level information of the image, and do not separately train the changed and unchanged areas according to the task characteristics of change detection. Also this model do not aggregate the deep and shallow-level feature information, so many local change details cannot be detected. This paper proposes a multilateral feature-guided aggregation network. The network firstly extracts the whole information, the changed area information and the unchanged area information of remote sensing image through the main network and two auxiliary networks: difference network and assimilation network. Then, the whole information of the image is aggregated with the changed and unchanged region information by feature aggregation network (all subnetworks are composed of convolutional neural networks (CNNs)). The proposed method is end-to-end trainable, and each component in the network does not need to be trained separately.