Remote Sensing Image Change Detection Research Articles

Multi-Scale Fusion Siamese Network Based on Three-Branch Attention Mechanism for High-Resolution Remote Sensing Image Change Detection

Remote sensing image change detection (CD) is an important means in remote sensing data analysis tasks, which can help us understand the surface changes in high-resolution (HR) remote sensing images. Traditional pixel-based and object-based methods are only suitable for low- and medium-resolution images, and are still challenging for complex texture features and detailed image detail processing in HR images. At present, the method based on deep learning has problems such as inconsistent fusion and difficult model training in the combination of the difference feature information of the deep and shallow layers and the attention mechanism, which leads to errors in the distinction between the changing region and the invariant region, edge detection and small target detection. In order to solve the above problems of inconsistent fusions of feature information aggregation and attention mechanisms, and indistinguishable change areas, we propose a multi-scale feature fusion Siamese network based on attention mechanism (ABMFNet). To tackle the issues of inconsistent fusion and alignment difficulties when integrating multi-scale fusion and attention mechanisms, we introduce the attention-based multi-scale feature fusion module (AMFFM). This module not only addresses insufficient feature fusion and connection between different-scale feature layers, but also enables the model to automatically learn and prioritize important features or regions in the image. Additionally, we design the cross-scale fusion module (CFM) and the difference feature enhancement pyramid structure (DEFPN) to assist the AMFFM module in integrating differential information effectively. These modules bridge the spatial disparity between low-level and high-level features, ensuring efficient connection and fusion of spatial difference information. Furthermore, we enhance the representation and inference speed of the feature pyramid by incorporating a feature enhancement module (FEM) into DEFPN. Finally, the BICD dataset proposed by the laboratory and public datasets LEVIR-CD and BCDD are compared and tested. We use F1 score and MIoU values as evaluation metrics. For AMBMFNet, the F1 scores on the three datasets are 77.69%, 81.57%, and 77.91%, respectively, while the MIoU values are 84.65%, 85.84%, and 84.54%, respectively. The experimental results show that ABMFNet has better effectiveness and robustness.

T-UNet: triplet UNet for change detection in high-resolution remote sensing images

ABSTRACT Remote sensing image change detection aims to identify differences between images acquired at different times in the same area, crucial for land management, environmental monitoring, and disaster assessment. Current change detection methods mainly use Siamese or early fusion structures. Siamese networks focus on object features at different times but lack attention to change information, leading to false alarms and missed detections. Early fusion structures focus on fused features but neglect temporal object features, hindering accurate change detection. To address these issues, we propose a novel network, Triplet UNet (T-UNet), employing a triplet encoder to simultaneously extract object features and change features between the pre- and post-time-phase images. To effectively interact features extracted from the three branches of triplet encoder, we propose a multi-branch spatial-spectral cross-attention module. In the decoder, we employ channel attention and spatial attention mechanisms to fully mine and integrate detailed texture and semantic localization information. The proposed T-UNet surpasses seven other state-of-the-art methods on three publicly available datasets. Extensive experiments verify the effectiveness of the proposed structure and modules as well as the superiority of the proposed T-UNet. The source code for the proposed T-UNet is accessible at https://github.com/Pl-2000/T-UNet.

Multistage Interaction Network for Remote Sensing Change Detection

Change detection in remote sensing imagery is vital for Earth monitoring but faces challenges such as background complexity and pseudo-changes. Effective interaction between bitemporal images is crucial for accurate change information extraction. This paper presents a multistage interaction network designed for effective change detection, incorporating interaction at the image, feature, and decision levels. At the image level, change information is directly extracted from intensity changes, mitigating potential change information loss during feature extraction. Instead of separately extracting features from bitemporal images, the feature-level interaction jointly extracts features from bitemporal images. By enhancing relevance to spatial variant information and shared semantic channels, the network excels in overcoming background complexity and pseudo-changes. The decision-level interaction combines image-level and feature-level interactions, producing multiscale feature differences for precise change prediction. Extensive experiments demonstrate the superior performance of our method compared to existing approaches, establishing it as a robust solution for remote sensing image change detection.

A Siamese Swin-Unet for image change detection

The problem of change detection in remote sensing image processing is both difficult and important. It is extensively used in a variety of sectors, including land resource planning, monitoring and forecasting of agricultural plant health, and monitoring and assessment of natural disasters. Remote sensing images provide a large amount of long-term and fully covered data for earth environmental monitoring. A lot of progress has been made thanks to deep learning's quick development. But the majority of deep learning-based change detection techniques currently in use rely on the well-known Convolutional neural network (CNN). However, considering the locality of convolutional operation, CNN unable to master the interplay between global and distant semantic information. Some researches has employ Vision Transformer as a backbone in remote sensing field. Inspired by these researches, in this paper, we propose a network named Siam-Swin-Unet, which is a Siamesed pure Transformer with U-shape construction for remote sensing image change detection. Swin Transformer is a hierarchical vision transformer with shifted windows that can extract global feature. To learn local and global semantic feature information, the dual-time image are fed into Siam-Swin-Unet which is composed of Swin Transformer, Unet Siamesenet and two feature fusion module. Considered the Unet and Siamesenet are effective for change detection, We applied it to the model. The feature fusion module is designed for fusion of dual-time image features, and is efficient and low-compute confirmed by our experiments. Our network achieved 94.67 F1 on the CDD dataset (season varying).

High-Resolution Remote Sensing Image Change Detection Based on Cross-Mixing Attention Network

With the powerful discriminative capabilities of convolutional neural networks, change detection has achieved significant success. However, current methods either ignore the spatiotemporal dependencies between dual-temporal images or suffer from decreased accuracy due to registration errors. Addressing these challenges, this paper proposes a method for remote sensing image change detection based on the cross-mixing attention network. To minimize the impact of registration errors on change detection results, a feature alignment module (FAM) is specifically developed in this study. The FAM performs spatial transformations on dual-temporal feature maps, achieving the precise spatial alignment of feature pairs and reducing false positive rates in change detection. Additionally, to fully exploit the spatiotemporal relationships between dual-temporal images, a cross-mixing attention module (CMAM) is utilized to extract global channel information, enhancing feature selection capabilities. Furthermore, attentional maps are created to guide the up-sampling process, optimizing feature information. Comprehensive experiments conducted on the LEVIR-CD and SYSU-CD change detection datasets demonstrate that the proposed model achieves F1 scores of 91.06% and 81.88%, respectively, outperforming other comparative models. In conclusion, the proposed model maintains good performance on two datasets and, thus, has good applicability in various change detection tasks.

MSGFNet: Multi-Scale Gated Fusion Network for Remote Sensing Image Change Detection

Change detection (CD) stands out as a pivotal yet challenging task in the interpretation of remote sensing images. Significant developments have been witnessed, particularly with the rapid advancements in deep learning techniques. Nevertheless, challenges such as incomplete detection targets and unsmooth boundaries remain as most CD methods suffer from ineffective feature fusion. Therefore, this paper presents a multi-scale gated fusion network (MSGFNet) to improve the accuracy of CD results. To effectively extract bi-temporal features, the EfficientNetB4 model based on a Siamese network is employed. Subsequently, we propose a multi-scale gated fusion module (MSGFM) that comprises a multi-scale progressive fusion (MSPF) unit and a gated weight adaptive fusion (GWAF) unit, aimed at fusing bi-temporal multi-scale features to maintain boundary details and detect completely changed targets. Finally, we use the simple yet efficient UNet structure to recover the feature maps and predict results. To demonstrate the effectiveness of the MSGFNet, the LEVIR-CD, WHU-CD, and SYSU-CD datasets were utilized, and the MSGFNet achieved F1 scores of 90.86%, 92.46%, and 80.39% on the three datasets, respectively. Furthermore, the low computational costs and small model size have validated the superior performance of the MSGFNet.

BD-MSA: Body Decouple VHR Remote Sensing Image Change Detection Method Guided by Multiscale Feature Information Aggregation

BD-MSA: Body Decouple VHR Remote Sensing Image Change Detection Method Guided by Multiscale Feature Information Aggregation

Context Spatial Awareness Remote Sensing Image Change Detection Network Based on Graph and Convolution Interaction

Context Spatial Awareness Remote Sensing Image Change Detection Network Based on Graph and Convolution Interaction

CGMMA: CNN-GNN Multiscale Mixed Attention Network for Remote Sensing Image Change Detection

CGMMA: CNN-GNN Multiscale Mixed Attention Network for Remote Sensing Image Change Detection

CLDRNet: A Difference Refinement Network Based on Category Context Learning for Remote Sensing Image Change Detection

CLDRNet: A Difference Refinement Network Based on Category Context Learning for Remote Sensing Image Change Detection

Domain Adaptive and Interactive Differential Attention Network for Remote Sensing Image Change Detection

Domain Adaptive and Interactive Differential Attention Network for Remote Sensing Image Change Detection

Remote Sensing Image Change Detection Combined with Saliency

Remote Sensing Image Change Detection Combined with Saliency

Lightweight Structure-Aware Transformer Network for Remote Sensing Image Change Detection

Lightweight Structure-Aware Transformer Network for Remote Sensing Image Change Detection

BiFA: Remote Sensing Image Change Detection With Bitemporal Feature Alignment

BiFA: Remote Sensing Image Change Detection With Bitemporal Feature Alignment

A Unified Deep Learning Network for Remote Sensing Image Registration and Change Detection

A Unified Deep Learning Network for Remote Sensing Image Registration and Change Detection

Transformer-based network with difference information aware attention module

Existing great progress has been made through remote sensing image change detection (CD) methods, which use convolutions and transformations appropriately. However, due to the complexity of the objects photographed on satellites, the out-of-phase of the same object may exhibit a fine feature diversity and a wide range of variations. Meanwhile, in the previous work, pure convolutional networks and Transformer networks that focus on local information perform better on non-local information. Therefore, current methods still have the problem of insufficient discrimination between irregular changes and diverse boundaries. Here, to solve this biphasic dilemma, we put forward a network that uses the difference information between bitemporal images as a guide to refine features. We use one feature map to subtract another and use multi-scale convolution to refine the difference map. We then designed an attention module similar to self-attention using differential information. In addition, considering the need for both local and global information in change detection, we use the full Swin Transformer framework with shift window self-attention to pass information between local areas. In addition, in order to enhance the network’s ability to recognize, members of our team reformulate supervised, traditional saliency map prediction through saliency and edge prediction. Notably, our proposed model significantly outperforms existing single-branch methods and state-of-the-art performance was achieved on four famous public datasets in terms of the F1-score index, interest in joint indices, and population indices.

Change Detection Needs Neighborhood Interaction in Transformer

Remote sensing image change detection (CD) is an essential technique for analyzing surface changes from co-registered images of different time periods. The main challenge in CD is to identify the alterations that the user intends to emphasize, while excluding pseudo-changes caused by external factors. Recent advancements in deep learning and image change detection have shown remarkable performance with ConvNet-based and Transformer-based techniques. However, ConvNet-based methods are limited by the local receptive fields of convolutional kernels that cannot effectively capture the change features in spatial–temporal information, while Transformer-based CD models need to be driven by a large amount of data due to the lack of inductive biases, and at the same time need to bear the costly computational complexity brought by self-attention. To address these challenges, we propose a Transformer-based Siamese network structure called BTNIFormer. It incorporates a sparse attention mechanism called Dilated Neighborhood Attention (DiNA), which localizes the attention range of each pixel to its neighboring context. Extensive experiments conducted on two publicly available datasets demonstrate the benefits of our proposed innovation. Compared to the most competitive recent Transformer-based approaches, our method achieves a significant 12.00% improvement in IoU while reducing computational costs by half. This provides a promising solution for further development of the Transformer structure in CD tasks.

A Full-Scale Connected CNN–Transformer Network for Remote Sensing Image Change Detection

Recent studies have introduced transformer modules into convolutional neural networks (CNNs) to solve the inherent limitations of CNNs in global modeling and have achieved impressive performance. However, some challenges have yet to be addressed: first, networks with simple connections between the CNN and transformer perform poorly in small change areas; second, networks that only use transformer structures are prone to attaining coarse detection results and excessively generalizing feature boundaries. In addition, the methods of fusing the CNN and transformer have the issue of a unilateral flow of feature information and inter-scale communication, leading to a loss of change information across different scales. To mitigate these problems, this study proposes a full-scale connected CNN–Transformer network, which incorporates the Siamese structure, Unet3+, and transformer structure, used for change detection in remote sensing images, namely SUT. A progressive attention module (PAM) is adopted in SUT to deeply integrate the features extracted from both the CNN and the transformer, resulting in improved global modeling, small target detection capacities, and clearer feature boundaries. Furthermore, SUT adopts a full-scale skip connection to realize multi-directional information flow from the encoder to decoder, enhancing the ability to extract multi-scale features. Experimental results demonstrate that the method we designed performs best on the CDD, LEVIR-CD, and WHU-CD datasets with its concise structure. In particular, based on the WHU-CD dataset, SUT upgrades the F1-score by more than 4% and the intersection over union (IOU) by more than 7% compared with the second-best method.

CSANet: a channel-spatial attention network for remote sensing image change detection

ABSTRACT Deep learning has revolutionized change detection (CD) in remote sensing tasks. However, high-resolution CD based on deep learning faces challenges in handling semantic complexity in bi-temporal images, especially across varying weather and lighting conditions. While CNN-based approaches optimize network structures to enhance contextual information, recent attention-based methods increase computational demands. This study introduces the Channel-Spatial Attention Network (CSANet) to extract multi-scale and semantic information from images. Evaluated on LEVIR-CD and DSIFN-CD datasets, CSANet outperforms several state-of-the-art methods, demonstrating its potential for advanced change detection in high-resolution remote sensing.

High‐resolution optical remote sensing image change detection based on dense connection and attention feature fusion network

AbstractThe detection of ground object changes from bi‐temporal images is of great significance for urban planning, land‐use/land‐cover monitoring and natural disaster assessment. To solve the limitation of incomplete change detection (CD) entities and inaccurate edges caused by the loss of detailed information, this paper proposes a network based on dense connections and attention feature fusion, namely Siamese NestedUNet with Attention Feature Fusion (SNAFF). First, multi‐level bi‐temporal features are extracted through a Siamese network. The dense connections between the sub‐nodes of the decoder are used to compensate for the missing location information as well as weakening the semantic differences between features. Then, the attention mechanism is introduced to combine global and local information to achieve feature fusion. Finally, a deep supervision strategy is used to suppress the problem of gradient vanishing and slow convergence speed. During the testing phase, the test time augmentation (TTA) strategy is adopted to further improve the CD performance. In order to verify the effectiveness of the proposed method, two datasets with different change types are used. The experimental results indicate that, compared with the comparison methods, the proposed SNAFF achieves the best quantitative results on both datasets, in which F1, IoU and OA in the LEVIR‐CD dataset are 91.47%, 84.28% and 99.13%, respectively, and the values in the CDD dataset are 96.91%, 94.01% and 99.27%, respectively. In addition, the qualitative results show that SNAFF can effectively retain the global and edge information of the detected entity, thus achieving the best visual performance.