As an important task in the field of remote sensing image processing, remote sensing image change detection (CD) has made significant advances through the use of convolutional neural networks (CNN). The Transformer has recently been introduced into the field of CD due to its excellent global perception capabilities. Some works have attempted to combine CNN and Transformer to jointly harvest local-global features. However, these works have not paid much attention to the interaction between the features extracted by both. Also, the use of the Transformer has resulted in significant resource consumption. In this paper, we propose the Asymmetric Cross-attention Hierarchical Network (ACAHNet) by combining CNN and Transformer in a series-parallel manners. The proposed Asymmetric Multi-headed Cross Attention (AMCA) module reduces the quadratic computational complexity of the Transformer to linear, and the module enhances the interaction between features extracted from the CNN and the Transformer. Different from the early and late fusion strategies employed in previous work, the effectiveness of the mid-term fusion strategy employed by ACAHNet shows a new choice of timing for feature fusion in the CD task. Our experiments on the proposed method on three public datasets show that our network has better performance in terms of effectiveness and computational resource consumption compared to other comparative methods.

As an important task in the field of remote sensing image interpretation, change detection (CD) has been extensively studied by scholars in recent years. Affected by the illumination and the environment during bitemporal images’ acquisition, there will be many pseudochanges, and the pseudochanges will seriously affect the effect of CD. Based on this, we propose a CD model named HMCNet, which introduces multilayer perceptron (MLP) into a convolutional neural network (CNN)-based CD model to form an MLP-CNN hybrid model. HMCNet has both the good feature extraction of CNN and the long-term dependence modeling ability of MLP, which can effectively overcome the interference of pseudochanges. In addition, the proposed cross-axis attention MLP can induce window attention of local features through shifted windows and, at the same time, form global attention to features through the interaction between information flows on the cross-axis, which effectively improves the comprehensive performance of MLP block. Extensive experiments on three public benchmark datasets show that HMCNet can achieve better performance with fewer parameters and Flops, and still maintain good generalization ability with fewer train data.

Change detection (CD) is a particularly important task in the field of remote sensing image processing. It is of practical importance for people when making decisions about transitional situations on the Earth’s surface. The existing CD methods focus on the design of feature extraction network, ignoring the strategy fusion and attention enhancement of the extracted features, which will lead to the problems of incomplete boundary of changed area and missing detection of small targets in the final output change map. To overcome the above problems, we proposed a hierarchical attention residual nested U-Net (HARNU-Net) for remote sensing image CD. First, the backbone network is composed of a Siamese network and nested U-Net. We remold the convolution block in nested U-Net and proposed ACON-Relu residual convolution block (A-R), which reduces the missed detection rate of the backbone network in small change areas. Second, this paper proposed the adjacent feature fusion module (AFFM). Based on the adjacency fusion strategy, the module effectively integrates the details and semantic information of multi-level features, so as to realize the feature complementarity and spatial mutual enhancement between adjacent features. Finally, the hierarchical attention residual module (HARM) is proposed, which locally filters and enhances the features in a more fine-grained space to output a much better change map. Adequate experiments on three challenging benchmark public datasets, CDD, LEVIR-CD and BCDD, show that our method outperforms several other state-of-the-art methods and performs excellent in F1, IOU and visual image quality.