Convolutional neural network (CNN) can extract effective semantic features, so it was widely used for remote sensing image change detection (CD) in the latest years. CNN has acquired great achievements in the field of CD, but due to the intrinsic locality of convolution operation, it could not capture global information in space-time. The transformer was proposed in recent years and it can effectively extract global information, so it was used to solve computer vision (CV) tasks and achieved amazing success. In this article, we design a pure transformer network with Siamese U-shaped structure to solve CD problems and name it SwinSUNet. SwinSUNet contains encoder, fusion, and decoder, and all of them use Swin transformer blocks as basic units. Encoder has a Siamese structure based on hierarchical Swin transformer, so encoder can process bitemporal images in parallel and extract their multiscale features. Fusion is mainly responsible for the merge operation of the bitemporal features generated by the encoder. Like encoder, the decoder is also based on hierarchical Swin transformer. Different from the encoder, the decoder uses upsampling and merging (UM) block and Swin transformer blocks to recover the details of the change information. The encoder uses patch merging and Swin transformer blocks to generate effective semantic features. After the sequential process of these three modules, SwinSUNet will output the change maps. We did expensive experiments on four CD datasets, and in these experiments, SwinSUNet achieved better results than other related methods.

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.

In recent years, advancements in remote-sensing image super-resolution have achieved remarkable performance. However, many methods demand significant computational resources. This is problematic for edge devices with limited computational capabilities. To alleviate this problem, we propose an attention-based multi-level feature fusion network (AMFFN) to enhance the resolution of remote-sensing images. This proposed network integrates three efficient design strategies to provide a lightweight solution. Initially, we design the partial shallow residual block (PSRB) to replace the redundant convolution operation. The PSRB optimizes feature extraction via partial convolution and capitalizes on information across channels using pointwise convolution. Subsequently, integrating the PSRB, our dynamic feature distillation block (DFDB) leverages an information distillation mechanism to distill and capture only the crucial features securing a robust feature depiction. Conclusively, for superior feature fusion, we conceptualized an attention-based multi-level feature fusion (AMFF) mechanism. The attention intrinsic to AMFF weighs the significance of features from varied branches, assuring that the resulting output is comprehensive and discerning. We conduct thorough experimental validation on two datasets of remote-sensing images and measure network complexity by evaluating network parameters and multi-adds operations. The results show that our method effectively balances computational complexity and performance. In addition, we have expanded the application of AMFFN to the field of natural image super-resolution. Experimental results on five benchmark test datasets further confirm the effectiveness of our method.

Presently, research on deep learning-based change detection (CD) methods has become a hot topic. In particular, feature pyramid networks (FPNs) are widely used in CD tasks to gradually fuse semantic features. However, existing FPN-based CD methods do not correctly detect the complete change region and cannot accurately locate the boundaries of the change region. To solve these problems, a new Multi-Scale Feature Progressive Fusion Network (MFPF-Net) is proposed, which consists of three innovative modules: Layer Feature Fusion Module (LFFM), Multi-Scale Feature Aggregation Module (MSFA), and Multi-Scale Feature Distribution Module (MSFD). Specifically, we first concatenate the features of each layer extracted from the bi-temporal images with their difference maps, and the resulting change maps fuse richer semantic information while effectively representing change regions. Then, the obtained change maps of each layer are directly aggregated, which improves the effective communication and full fusion of feature maps in CD while avoiding the interference of indirect information. Finally, the aggregated feature maps are layered again by pooling and convolution operations, and then a feature fusion strategy with a pyramid structure is used, with layers fused from low to high, to obtain richer contextual information, so that each layer of the layered feature maps has original semantic information and semantic features of other layers. We conducted comprehensive experiments on three publicly available benchmark datasets, CDD, LEVIR-CD, and WHU-CD to verify the effectiveness of the method, and the experimental results show that the method in this paper outperforms other comparative methods.

Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures.