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Deep learning has achieved great success in remote sensing image change detection (CD). However, most methods focus only on the changed regions of images and cannot accurately identify their detailed semantic categories. In addition, most CD methods using convolutional neural networks (CNN) have difficulty capturing sufficient global information from images. To address the above issues, we propose a novel symmetric multi-task network (SMNet) that integrates global and local information for semantic change detection (SCD) in this paper. Specifically, we employ a hybrid unit consisting of pre-activated residual blocks (PR) and transformation blocks (TB) to construct the (PRTB) backbone, which obtains more abundant semantic features with local and global information from bi-temporal images. To accurately capture fine-grained changes, the multi-content fusion module (MCFM) is introduced, which effectively enhances change features by distinguishing foreground and background information in complex scenes. In the meantime, the multi-task prediction branches are adopted, and the multi-task loss function is used to jointly supervise model training to improve the performance of the network. Extensive experimental results on the challenging SECOND and Landsat-SCD datasets, demonstrate that our SMNet obtains 71.95% and 85.65% at mean Intersection over Union (mIoU), respectively. In addition, the proposed SMNet achieves 20.29% and 51.14% at Separated Kappa coefficient (Sek) on the SECOND and Landsat-SCD datasets, respectively. All of the above proves the effectiveness and superiority of the proposed method.

Semantic change detection (SCD) is a challenging task in remote sensing, which aims to locate and identify changes between the bi-temporal images, providing detailed “from-to” change information. This information is valuable for various remote sensing applications. Recent studies have shown that multi-task networks, with dual segmentation branches and single change branch, are effective in SCD tasks. However, these networks primarily focus on extracting contextual information and ignore spatial details, resulting in the missed or false detection of small targets and inaccurate boundaries. To address the limitations of the aforementioned methods, this paper proposed a spatial-temporal semantic perception network (STSP-Net) for SCD. It effectively utilizes spatial detail information through the detail-aware path (DAP) and generates spatial-temporal semantic-perception features through combining deep contextual features. Meanwhile, the network enhances the representation of semantic features in spatial and temporal dimensions by leveraging a spatial attention fusion module (SAFM) and a temporal refinement detection module (TRDM). This augmentation results in improved sensitivity to details and adaptive performance balancing between semantic segmentation (SS) and change detection (CD). In addition, by incorporating the invariant consistency loss function (ICLoss), the proposed method constrains the consistency of land cover (LC) categories in invariant regions, thereby improving the accuracy and robustness of SCD. The comparative experimental results on three SCD datasets demonstrate the superiority of the proposed method in SCD. It outperforms other methods in various evaluation metrics, achieving a significant improvement. The Sek improvements of 2.84%, 1.63%, and 0.78% have been observed, respectively.

With the advent of very-high-resolution remote sensing images, semantic change detection (SCD) based on deep learning has become a research hotspot in recent years. SCD aims to observe the change in the Earth’s land surface and plays a vital role in monitoring the ecological environment, land use and land cover. Existing research mainly focus on single-task semantic change detection; the problem they face is that existing methods are incapable of identifying which change type has occurred in each multi-temporal image. In addition, few methods use the binary change region to help train a deep SCD-based network. Hence, we propose a dual-task semantic change detection network (GCF-SCD-Net) by using the generative change field (GCF) module to locate and segment the change region; what is more, the proposed network is end-to-end trainable. In the meantime, because of the influence of the imbalance label, we propose a separable loss function to alleviate the over-fitting problem. Extensive experiments are conducted in this work to validate the performance of our method. Finally, our work achieves a 69.9% mIoU and 17.9 Sek on the SECOND dataset. Compared with traditional networks, GCF-SCD-Net achieves the best results and promising performances.

Semantic Change Detection (SCD) aims to accurately identify the change areas and their categories in dual-time images, which is more complex and challenging than traditional binary change detection tasks. Accurately capturing the change information of land cover types is crucial for remote sensing image analysis and subsequent decision-making applications. However, existing SCD methods often neglect the spatial details and temporal dependencies of dual-time images, leading to problems such as change category imbalance and limited detection accuracy, especially in capturing small target changes. To address this issue, this study proposes a network that guides multitask semantic change detection through spatiotemporal semantic interaction (STGNet). STGNet enhances the ability to capture spatial details by introducing a Detail-Aware Path (DAP) and designs a Bidirectional Guidance Module for Spatial Detail and Semantic Information for adaptive feature selection, improving feature extraction capabilities in complex scenes. Furthermore, to resolve the inconsistency between semantic information and change areas, this paper designs a Cross-Temporal Refinement Interaction Module (CTIM), which enables cross-time scale feature fusion and interaction, constraining the consistency of detection results and improving the recognition accuracy of unchanged areas. To further enhance detection performance, a dynamic depthwise separable convolution is designed in the CTIM module, which can adaptively adjust convolution kernels to more precisely capture change features in different regions of the image. Experimental results on three SCD datasets show that the proposed method outperforms other existing methods in various evaluation metrics. In particular, on the Landsat-SCD dataset, the F1 score (F1 scd ) reaches 91.64%, and the separation Kappa coefficient improves by 17.68%. These experimental results fully demonstrate the significant advantages of STGNet in improving semantic change detection accuracy, robustness, and generalization capability.

What is the main finding? * A three-branch model named HCTANet is proposed for remote sensing semantic change detection (SCD), which innovatively integrates three core modules: the multi-scale change mapping association (MCA) module (parallelly extracts and fuses multi-resolution dual-temporal difference features, and uses binary change detection (BCD) output to constrain semantic segmentation), the adaptive collaborative semantic attention (ACo-SA) mechanism (models cross-temporal feature semantic correlations via dynamic weight fusion and cross-window self-attention), and the spatial semantic residual aggregation (SSRA) module (fuses global context with high-resolution shallow features through residuals to restore pixel-level boundaries). A three-branch model named HCTANet is proposed for remote sensing semantic change detection (SCD), which innovatively integrates three core modules: the multi-scale change mapping association (MCA) module (parallelly extracts and fuses multi-resolution dual-temporal difference features, and uses binary change detection (BCD) output to constrain semantic segmentation), the adaptive collaborative semantic attention (ACo-SA) mechanism (models cross-temporal feature semantic correlations via dynamic weight fusion and cross-window self-attention), and the spatial semantic residual aggregation (SSRA) module (fuses global context with high-resolution shallow features through residuals to restore pixel-level boundaries). What are the implications of the main findings? * The model effectively addresses key issues in remote sensing SCD, such as insufficient information interaction, single-scale feature limitations, and unbalanced long-range/local details, providing a reliable solution for accurate SCD in complex scenarios. * HCTANet’s design ideas (multi-scale fusion, cross-temporal attention, residual aggregation) offer a reference for optimizing SCD models, and its performance on self-constructed AirFC dataset supports the expansion of SCD applications to professional fields (e.g., urban development, airport infrastructure dynamic monitoring). The model effectively addresses key issues in remote sensing SCD, such as insufficient information interaction, single-scale feature limitations, and unbalanced long-range/local details, providing a reliable solution for accurate SCD in complex scenarios. HCTANet’s design ideas (multi-scale fusion, cross-temporal attention, residual aggregation) offer a reference for optimizing SCD models, and its performance on self-constructed AirFC dataset supports the expansion of SCD applications to professional fields (e.g., urban development, airport infrastructure dynamic monitoring). Semantic change detection has become a key technology for monitoring the evolution of land cover and land use categories at the semantic level. However, existing methods often lack effective information interaction and fail to capture changes at multiple granularities using single-scale features, resulting in inconsistent outcomes and frequent missed or false detections. To address these challenges, we propose a three-branch model HCTANet, which enhances spatial and semantic feature representations at each time stage and models semantic correlations and differences between multi-temporal images through three innovative modules. First, the multi-scale change mapping association module extracts and fuses multi-resolution dual-temporal difference features in parallel, explicitly constraining semantic segmentation results with the change area output. Second, an adaptive collaborative semantic attention mechanism is introduced, modeling the semantic correlations of dual-temporal features via dynamic weight fusion and cross-temporal cross-attention. Third, the spatial semantic residual aggregation module aggregates global context and high-resolution shallow features through residual connections to restore pixel-level boundary details. HCTANet is evaluated on the SECOND, SenseEarth 2020 and AirFC datasets, and the results show that it outperforms existing methods in metrics such as mIoU and SeK, demonstrating its superior capability and effectiveness in accurately detecting semantic changes in complex remote sensing scenarios.

Semantic change detection (SCD) holds a critical place in remote sensing image interpretation, as it aims to locate changing regions and identify their associated land cover classes. Presently, post-classification techniques stand as the predominant strategy for SCD due to their simplicity and efficacy. However, these methods often overlook the intricate relationships between alterations in land cover. In this paper, we argue that comprehending the interplay of changes within land cover maps holds the key to enhancing SCD’s performance. With this insight, a Temporal-Transform Module (TTM) is designed to capture change relationships across temporal dimensions. TTM selectively aggregates features across all temporal images, enhancing the unique features of each temporal image at distinct pixels. Moreover, we build a Temporal-Transform Network (TTNet) for SCD, comprising two semantic segmentation branches and a binary change detection branch. TTM is embedded into the decoder of each semantic segmentation branch, thus enabling TTNet to obtain better land cover classification results. Experimental results on the SECOND dataset show that TTNet achieves enhanced performance when compared to other benchmark methods in the SCD task. In particular, TTNet elevates mIoU accuracy by a minimum of 1.5% in the SCD task and 3.1% in the semantic segmentation task.

Change detection (CD) is the main task in the remote sensing field. Binary change detection (BCD), which only focuses on the region of change, cannot meet current needs. Semantic change detection (SCD) is pivotal for identifying regions of change in sequential remote sensing imagery, focusing on discerning “from-to” transitions in land cover. The emphasis on features within these regions of change is critical for SCD efficacy. Traditional methodologies, however, often overlook this aspect. In order to address this gap, we introduce a change-aware guided multi-task network (CGMNet). This innovative network integrates a change-aware mask branch, leveraging prior knowledge of regions of change to enhance land cover classification in dual temporal remote sensing images. This strategic focus allows for the more accurate identification of altered regions. Furthermore, to navigate the complexities of remote sensing environments, we develop a global and local attention mechanism (GLAM). This mechanism adeptly captures both overarching and fine-grained spatial details, facilitating more nuanced analysis. Our rigorous testing on two public datasets using state-of-the-art methods yielded impressive results. CGMNet achieved Overall Score metrics of 58.77% on the Landsat-SCD dataset and 37.06% on the SECOND dataset. These outcomes not only demonstrate the exceptional performance of the method but also signify its superiority over other comparative algorithms.

Semantic change detection (SCD) effectively captures ground object transition information within change regions, delivering more comprehensive and detailed results than binary change detection (BCD) tasks. The existing multi-task SCD models enable parallel processing of segmentation and BCD of bi-temporal remote sensing images, but they still have shortcomings in feature mining, interaction, and cross-task transfer. To address these limitations, a multi-branch feature interaction network (MBFI-Net) is proposed. MBFI-Net designs parallel encoding branches with attention mechanisms that enhance semantic change perception by jointly modeling global contextual patterns and local details. In addition, MBFI-Net proposes bi-temporal feature interaction (BTFI) and cross-task feature transfer (CTFT) modules to improve feature diversity and representativeness, and combines with prior logical relationship constraints to improve SCD performance. Comparative and ablation studies on the SECOND and Landsat-SCD datasets highlight the superiority and robustness of MBFI-Net, which achieves SeKs of 0.2117 and 0.5543, respectively. Furthermore, MBFI-Net strikes a balance between SCD results and model complexity and has superior detection performance for semantic change categories with a small proportion.

It is challenging to investigate semantic change detection (SCD) in bi-temporal high-resolution (HR) remote sensing images. For the non-changing surfaces in the same location of bi-temporal images, existing SCD methods often obtain the results with frequent errors or incomplete change detection due to insufficient performance on overcoming the phenomenon of intraclass differences. To address the above-mentioned issues, we propose a novel multi-task consistency enhancement network (MCENet) for SCD. Specifically, a multi-task learning-based network is constructed by combining CNN and Transformer as the backbone. Moreover, a multi-task consistency enhancement module (MCEM) is introduced, and cross-task mapping connections are selected as auxiliary designs in the network to enhance the learning of semantic consistency in non-changing regions and the integrity of change features. Furthermore, we establish a novel joint loss function to alleviate the negative effect of class imbalances in quantity during network training optimization. We performed experiments on publicly available SCD datasets, including the SECOND and HRSCD datasets. MCENet achieved promising results, with a 22.06% Sek and a 37.41% Score on the SECOND dataset and a 14.87% Sek and a 30.61% Score on the HRSCD dataset. Moreover, we evaluated the applicability of MCENet on the NAFZ dataset that was employed for cropland change detection and non-agricultural identification, with a 21.67% Sek and a 37.28% Score. The relevant comparative and ablation experiments suggested that MCENet possesses superior performance and effectiveness in network design.

Semantic change detection (SCD) in remote sensing image aims to identify semantic alterations between bi-temporal images captured at the same geographic location. SCD is extensively applied in fields such as environmental monitoring and disaster assessment. Despite significant advancements in deep learning leading to numerous successful approaches, most existing methods primarily rely on visual representation learning, thereby overlooking the potential benefits of multimodal data. Recently, vision-language models have demonstrated outstanding performance across various downstream tasks. In this paper, we propose a novel framework named TextSCD that leverages text-based semantic information to guide the generation of semantic change maps. Our approach integrates Gemini to generate change descriptions between bi-temporal images and employs a multi-level semantic extraction method to capture features from both images and their corresponding captions. Furthermore, we introduce a semantic text-guided interaction module that facilitates the effective integration of visual and textual features, enhancing multimodal knowledge transfer and the extraction of discriminative features. This design effectively reduces false detections and omissions. We validate the effectiveness of our model on the SECOND dataset, achieving notable improvements in overall accuracy for semantic change detection.