ACMFNet: Asymmetric Convolutional Feature Enhancement and Multiscale Fusion Network for Change Detection

Existing deep-learning supervised change detection (CD) networks still have room for improvement, as they do not fully utilize multiscale features in their feature extraction, resulting in insufficient feature representation ability and edge blurring problems of the constructed CD networks. In this article, we proposed a very high-resolution (VHR) remote sensing image CD network (ACMFNet) with an asymmetric convolution residual block (ACRB) and multiscale fusion (MSF) to improve the feature representation ability of the CD network and alleviate the edge blurring problem. ACMFNet consists of two main subnetworks: an ACRB feature extraction encoder and an MSF decoder. The ACRB is constructed based on asymmetric convolution and focuses on extraction at the edges of the features and is more robust to rotations, flip distortions, and uneven aspect ratios of the features. In the designed MSF decoder, the fusion feature maps of each level of the decoder are generated by fusing the multiscale encoder feature maps and feature map of the next lowest level of the decoder. MSF contributes to the reconstruction of the edge change area by combining feature information at different scales. It was tested on three public VHR remote sensing image CD datasets, and the proposed method demonstrates the best recall and F1-scores, as well as near optimal precision.