CGLCS-Net: Addressing Multi-Temporal and Multi-Angle Challenges in Remote Sensing Change Detection.

Remote sensing image change detection (RSICD) is an essential measure for monitoring the earth’s surface changes. In recent years, the explosive growth of very high-resolution (VHR) satellite sensors and the booming innovations in deep learning technology have significantly boosted RSICD development. However, most of the current RSICD models focus on locating accurate change areas while ignoring the efficiency of their method, which limits the practical application of RSICD models, especially for large-scale and emergency RSICD tasks. In this paper, we propose an Efficient Multi-scale-fusion Change Detection Network (EMS-CDNet) for bi-temporal RSICD tasks. Our EMS-CDNet pays more attention to the model’s inference speed and the accuracy-efficiency trade-off rather than only pursuing detection accuracy. We designed a multi-scale fusion module for EMS-CDNet, which adopts multi-scale and multi-branch operations to extract multi-scale features simultaneously and aggregate features at different feature levels. In addition to EMS-CDNet’s ability to achieve sufficient feature extraction, the multi-scale image input within the designed module alleviates the influence of image registration errors in practical applications, thereby strengthening EMS-CDNet’s value for practical RSICD tasks. We also integrated a novel partition unit in EMS-CDNet to lighten the model while maintaining the detection ability of small targets, thus shortening its processing time without a severe accuracy decrease. We conducted experiments on two state-of-the-art (SOTA) public RSICD datasets and our own collected dataset. The public datasets were utilized to comparatively measure the overall accuracy and efficiency measurement of EMS-CDNet, and the dataset of images we collected was used to observe EMS-CDNet’s performance under the influence of image registration errors. Our experimental results show that EMS-CDNet achieved a better accuracy-efficiency trade-off than the SOTA public datasets methods. For example, EMS-CDNet reduced the inference time by about 33% while maintaining identical detection accuracy to CLNet (the optimal method among the comparison methods). Furthermore, EMS-CDNet achieved higher accuracy on our collected dataset, with an F1 of 74% and mIoU of 0.806, demonstrating its robustness to image registration errors and showing its value for practical RSICD applications.

Generative adversarial networks (GANs) possess powerful image translation capabilities. They can transform images acquired from different sensors into a unified domain, effectively mitigating the incomparability problem caused by imaging discrepancies in multimodal remote sensing change detection (CD). However, existing approaches predominantly emphasize domain unification while neglecting the loss of fine-grained features inherent in the translation process, consequently compromising both image translation quality and CD accuracy. To overcome these limitations, we propose a novel texture and structure interaction guided GAN (TSIG-GAN). This network establishes interactive guidance between image texture and structural features through a carefully designed dual-stream cross encoder-decoder architecture, enabling in-depth mining of fine-grained features and significantly improving the fidelity of translated images. Furthermore, to address the spatial scale diversity and complexity of remote sensing images, we develop a multi-scale adaptive feature pyramid (MAFP) module and a contextual semantic interaction guidance (CSIG) mechanism, aiming to further strengthen the model's robust representation of fine-grained features across multiple scales and complex scenes. Specifically, the MAFP module effectively captures spatial details of targets at different resolutions by dynamically integrating multi-scale features, thereby preventing detail loss in small objects due to scale discrepancies. The CSIG mechanism achieves deep interaction between texture and structural features at the contextual semantic level, further promoting their mutual cooperation, thereby enhancing the consistency of fine-grained features representation and semantic integrity in complex scenes. Finally, the translated fine-grained images are fed into a custom CD network to extract changes. To evaluate the effectiveness of the proposed method, we conducted systematic experiments on five representative real-world datasets and performed comparative analysis with sixteen state-of-the-art multimodal CD methods. The experimental results demonstrate that TSIG-GAN achieves significant improvements in both image translation and CD performance, exhibiting superior fine-grained restoration capability and change identification capability.

Remote sensing image change detection is the detection process of determining the surface change area and change feature type for the same image area from multiple time series remote sensing data. It is the core technical means of land use change detection and land cover change detection, and it is also the key research field of remote sensing applied science. In view of the problems of time-consuming, labor-intensive, and low detection accuracy in the past image change detection methods, researchers in related fields have proposed more and more cutting-edge remote sensing image change detection methods. This article first describes the development of remote sensing image change detection methods, then explains the conventional processing flow of change detection and the conventional methods of change monitoring, and finally discusses the research progress of more mainstream change detection methods.

ABSTRACTIn this article, we propose a novel difference image (DI) creation method for unsupervised change detection in multi-temporal multi-spectral remote-sensing images based on deep learning theory. First, we apply deep belief network to learn local and high-level features from the local neighbour of a given pixel in an unsupervised manner. Second, a back propagation algorithm is improved to build a DI based on selected training samples, which can highlight the difference on changed regions and suppress the false changes on unchanged regions. Finally, we get the change trajectory map using simple clustering analysis. The proposed scheme is tested on three remote-sensing data sets. Qualitative and quantitative evaluations show its superior performance compared to the traditional pixel-level and texture-level-based approaches.

In modern remote sensing image change detection (CD), convolution Neural Network (CNN), especially U-shaped structure (UNet), has achieved great success due to powerful discriminative ability. However, UNet-based CNN networks usually have limitations in modeling global dependencies due to intrinsic locality of convolution operations. Transformer has recently emerged as an alternative architecture for dense prediction tasks due to global self-attention mechanism. However, due to limitation of hardware resources, pure Transformer methods generally lack the ability to capture global information at a low level. Based on these existing problems, we propose STransUNet, which combines Transformer and UNet architecture. STransUNet can not only capture shallow detail features at an early stage, but also model global context in high-level feature. In addition, we design an efficient feature fusion module named Cross-Enhanced Adaptive Fusion(CEAF). Our model mainly consists of three parts: encoder, fusion module and decoder. The decoder is a CNN-Transformer hybrid structure. CNN extracts multi-level feature information. Transformer encodes tokenized sequence to capture global context. CEAF module cross-enhances and adaptively fuses bi-temporal features to enhance feature representation. In decoding stage, we introduce a Cascaded Upsampling decoder(CUP). CUP progressively aggregates low-level CNN features and high-level Transformer features to full resolution. On four public CD datasets, our STransUNet achieves better CD results than six state-of-the-art algorithms.

Multi-scale change monitoring of water environment using cloud computing in optimal resolution remote sensing images

Deep learning plays a highly essential role in the domain of remote sensing change detection (CD) due to its high efficiency. From some existing methods, we can observe that the fusion of information at each scale is quite vital for the accuracy of the CD results, especially for the common problems of pseudo-change and the difficult detection of change edges in the CD task. With this in mind, we propose a New Fusion network with Dual-branch Encoder and Triple-branch Decoder (DETDNet) that follows a codec structure as a whole, where the encoder adopts a siamese Res2Net-50 structure to extract the local features of the bitemporal images. As for the decoder in previous works, they usually employed a single branch, and this approach only preserved the fusion features of the encoder’s bitemporal images. Distinguished from these approaches, we adopt the triple-branch architecture in the decoder for the first time. The triple-branch structure preserves not only the dual-branch features from the encoder in the left and right branches, respectively, to learn the effective and powerful individual features of each temporal image but also the fusion features from the encoder in the middle branch. The middle branch utilizes triple-branch aggregation (TA) to realize the feature interaction of the three branches in the decoder, which enhances the integrated features and provides abundant and supplementary bitemporal feature information to improve the CD performance. The triple-branch architecture of the decoder ensures that the respective features of the bitemporal images as well as their fused features are preserved, making the feature extraction more integrated. In addition, the three branches employ a multiscale feature extraction module (MFE) per layer to extract multiscale contextual information and enhance the feature representation capability of the CD. We conducted comparison experiments on the BCDD, LEVIR-CD, and SYSU-CD datasets, which were created in New Zealand, the USA, and Hong Kong, respectively. The data were preprocessed to contain 7434, 10,192, and 20,000 image pairs, respectively. The experimental results show that DETDNet achieves F1 scores of 92.7%, 90.99%, and 81.13%, respectively, which shows better results compared to some recent works, which means that the model is more robust. In addition, the lower FP and FN indicate lower error and misdetection rates. Moreover, from the analysis of the experimental results, compared with some existing methods, the problem of pseudo-changes and the difficulty of detecting small change areas is better solved.

Remote sensing image change detection (RS-CD) has made impressive progress with the help of deep learning techniques. Small object change detection (SoCD) still faces many challenges. On the one hand, when the scale of changing objects varies greatly, deep learning models with overall accuracy as the optimization goal tend to focus on large object changes and ignore small object changes to some extent. On the other hand, the RS-CD model based on deep convolutional networks needs to perform multiple spatial pooling operations on the feature map to obtain deep semantic features, which leads to the loss of small object feature-level information in the local space. Therefore, we propose a Siamese transformer change detection network with a multiscale window via an adaptive fusion strategy (SWaF-Trans). To solve the problem of ignoring small object changes, we compute self-attention in windows of different scales to model changing objects at the corresponding scales and establish semantic information links through a moving window mechanism to capture more comprehensive small object features in small-scale windows, thereby enhancing the feature representation of multiscale objects. To fuse multiscale features and alleviate the problem of small object feature information loss, we propose a channel-related fusion mechanism to model the global correlation between channels for display and adaptively adjust the fusion weights of channels to enable the network to capture more discriminative features of interest and reduce small object feature information loss. Experiments on the CDD and WHU-CD datasets show that SWaF-Trans exceeds eight advanced baseline methods, with absolute F1 scores as high as 97.10% and 93.90%, achieving maximum increases of 2% and 5.6%, respectively, compared to the baseline methods.

This paper proposes a novel framework for very high resolution remote sensing image change detection. The change detection technology is the goals or the phenomenon conditions of different time interval to the change that have analysed the recognition and computer image processing system, including judgement goal whether changes, to determine changes the region and the time and spatial distribution of pattern category and appraisal change of distinction change. Over the past few years, researchers from all over the world have devoted themselves to the research of change detection technology. Many detection methods based on remote sensing images have been developed successively. However, no change detection method has absolute superiority in present research. This paper obtains the inspiration from PSO and OTSU to propose the particle swarm optimisation segmentation jointed model to construct the optimal solution of generating change map and the PSO jointed OTSU is introduced to help obtain the optimal threshold. Numerical simulation proves that the proposed method can segment the changed regions accurately while keeping the high noise robustness.

At present, most remote sensing change detection methods are applicable to bi-temporal image alignment scenarios, that is, assuming that the pixel pairs of bi-temporal images are spatially registered. Detection accuracy is highly sensitive to the alignment accuracy of the image pairs. In practical applications, obtaining well-registered image pairs is often challenging. Currently, the approach of aligning first and then detecting is both inefficient and expensive. Explicitly integrating image alignment and change detection into a framework is an effective solution. However, offset information is difficult to be reflected in the high-level features of the image, it is hard to predict accurate image offsets, and it is also difficult to correct the spatial relationship of land covers in the image using the offset. To overcome the above problems, we propose a residual offset-driven feature alignment network (ROFANet). ROFANet combines two innovative methods: residual offset prediction (ROP) and dual-branch feature correction (DFC). ROP utilizes multi-level features to achieve offset prediction from coarse to fine granularity, effectively enhancing the model's predictive ability for image offsets. DFC has established two branches: image correction and feature correction, which respectively correct distorted images and distorted features. By optimizing the spatial relationship representation of land covers, the model's change detection ability under unaligned image conditions has been enhanced. Extensive experiments conducted on three publicly available change detection datasets demonstrate that the proposed ROFANet achieves outstanding detection performance in unaligned image scenarios. The source code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/bitwgq/ROFANet</uri>.

This study presents a new end-to-end change detection network, called difference-enhancement dense-attention convolutional neural network (DDCNN), that is designed for detection of changes in the bitemporal optical remote sensing images. To model the internal correlation between high-level and low-level features, a dense attention method consisting of several up-sampling attention units is proposed. Both the up-sampling spatial and up-sampling channel attention are adopted by the unit. The unit, which can use high-level features with rich category information to guide the selection of low-level features, can use the spatial context information to capture the changed features of ground objects. Furthermore, DDCNN also pays attention to the differentiating features of the bitemporal images. By introducing a DE unit, each pixel is weighted and the features are selectively aggregated. The combination of dense attention and the DE unit improves the effectiveness of the network and its accuracy in extracting the change features. The effectiveness of the proposed approach is demonstrated via five challenge data sets. The experimental results show that DDCNN achieves new state-of-the-art change detection performance on these five challenging data sets. For the seasonal change detection data set in particular, compared with the best existing change detection model, the proposed method increases the F1 score and IoU by 2.96% and 5.17%, respectively; compared with the baseline method, our method improved 3.75% and 6.50% on the F1 score and IoU, respectively.

Multi-temporal remote sensing image change detection is one of the important contents of remote sensing image processing, and has important applications in many fields. Existing multi-temporal change detection mainly deals with bi-temporal images and extracts change information by ratio or difference method. This processing cannot effectively mine the change information between multiple temporal images or time series of remote sensing images. In this paper, a two-layer slow feature analysis network (SFANet) is proposed to realize effective change detection with multiple temporal remote sensing images. The proposed method extends the existing slow feature analysis method to a two-layer network structure and forms a slow feature analysis network. On the basis of multi-temporal feature extraction in slow feature analysis network, change detection is realized by threshold method. In this paper, multi-temporal high-resolution remote sensing images are used for experiments. The experimental results demonstrate that the proposed SFANet for change detection is effective and better than several commonly used methods.

Remote sensing change detection (RSCD) plays a crucial role in environmental monitoring, resource management, and land planning. However, remote sensing images typically involve multiple surface types, and the similarities and differences between these types make change detection in complex scenes even more challenging. Therefore, extracting and integrating effective features in complex scenes becomes a key issue in RSCD. In recent years, SAM2 and Mamba have made a significant impact in the field of computer vision, but further research is needed in the remote sensing domain to fully leverage their advantages. Thus, this paper proposes a two-stage change detection network named SAM-Mamba. Specifically, we use SAM2 as the encoder to extract key features from remote sensing images and perform preliminary decoding to generate mixed prompts, which are then passed to the guiding encoder (PE) to generate accurate and stable feature boundaries. These boundaries are then fused with the key features and passed to the decoder. In the decoding stage, we integrate the Mamba model, which is designed to efficiently model long sequences and capture spatiotemporal correlations. Furthermore, to enhance the ability to capture complex changes, we design the Frequency-guided Feature Refinement (FGFR) module and Dynamic Feature Fusion (DFM) module. Our SAM-Mamba outperforms other state-of-the-art methods on four datasets (e.g., Intersection over Union (IoU) and F1-score are improved by 3.40%/2.96%, 1.02%/1.07% and 2.52%/2.42% on LEVIR-CD+, WHU-CD and SYSU-CD datasets, respectively). The code of SAM-Mamba are publicly released at https://github.com/lytwxyyyds/Sam-Mamba.

As a significant means of Earth observation, change detection in high-resolution remote sensing images has received extensive attention. Nevertheless, the variability in imaging conditions introduces style discrepancies and a range of pseudochange regions between bitemporal image pairs. Furthermore, changing objects possess diverse morphological representations, which makes accurately identifying change areas and delineating their boundaries within complex object distributions increasingly difficult. In response to the aforementioned challenges, we propose the Fourier feature interaction and multiscale perception (FIMP) model for effective change detection. To mitigate the impact of style discrepancies, FIMP employs the Fourier transform to adaptively filter bitemporal features in the frequency domain while mining the optimized bitemporal features relevant to the change detection task. To enhance the ability to recognize multiscale changing objects, FIMP aggregates and emphasizes the change areas with the introduced temporal change enhancement module (TCEM). By utilizing the U-fusion change perception module (UCPM) to perform multilevel bidirectional fusion of change features at different scales, FIMP can further enhance the ability to delineate complex semantic change boundaries. Experiments on three public datasets show that our approach outperforms seven state-of-the-art methods.

Deep Neural Network (DNN) has been widely used in remote sensing image change detectionCD in recent years. Due to the scarcity of training data, a large number of labeled data onto other fields become the source of deep neural network concept learning in remote sensing image change detection. However, the distribution of features of the change detection data and other data varies greatly, which prevents DNN from being better applied for one task to another. To solve this problem, a domain adaptive CD method based on segmentation map difference is proposed to this paper, which includes the pre-training stage and the change detection stage. In the pre-training stage, the domain adaptive UNet (Ada-UNet) is applied as the basic network of remote sensing image segmentation for network training, with the purpose of learning the concepts of different features. In the change detection stage, strict threshold segmentation results are used to train the channel attention network, which makes it more efficient to utilize the high-dimensional feature map. The probabilistic map generated by the three-channel attention networks is evaluated, and then it is used to accurately classify the changing pixels. In this paper, experiments are carried out on datasets with different feature distributions. The results show that this method has strong domain adaptability and can greatly reduce the influence of the difference in feature distributions of the CD results.