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State-of-the-art models in semantic segmentation primarily operate on single, static images, generating corresponding segmentation masks. This one-shot approach leaves little room for error correction, as the models lack the capability to integrate multiple observations for enhanced accuracy. Inspired by work on semantic change detection, we address this limitation by introducing a methodology that leverages a sequence of observables generated for each static input image. By adding this "temporal" dimension, we exploit strong signal correlations between successive observations in the sequence to reduce error rates. Our framework, dubbed SSG2 (Semantic Segmentation Generation 2), employs a dual-encoder, single-decoder base network augmented with a sequence model. The base model learns to predict the set intersection, union, and difference of labels from dual-input images. Given a fixed target input image and a set of support images, the sequence model builds the predicted mask of the target by synthesizing the partial views from each sequence step and filtering out noise. We evaluate SSG2 across four diverse datasets: UrbanMonitor, featuring orthoimage tiles from Darwin, Australia with four spectral bands at 0.2 m spatial resolution and a surface model; ISPRS Potsdam, which includes true orthophoto images with multiple spectral bands and a 5 cm ground sampling distance; ISPRS Vahingen, which also includes true orthophoto images and a 9 cm ground sampling distance; and ISIC2018, a medical dataset focused on skin lesion segmentation, particularly melanoma. The SSG2 model demonstrates rapid convergence within the first few tens of epochs and significantly outperforms UNet-like baseline models with the same number of gradient updates. However, the addition of the temporal dimension results in an increased memory footprint. While this could be a limitation, it is offset by the advent of higher-memory GPUs and coding optimizations. Our code is available at https://github.com/feevos/ssg2. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Current semantic change detection (SCD) methods face challenges in modeling temporal correlations (TCs) between bitemporal semantic features and difference features. These methods lead to inaccurate detection results, particularly for complex SCD scenarios. This paper presents a hierarchical semantic graph interaction network (HGINet) for SCD from high-resolution remote sensing images. This multitask neural network combines semantic segmentation and change detection tasks. For semantic segmentation, we construct a multilevel perceptual aggregation network with a pyramidal architecture. It extracts semantic features that discriminate between different categories at multiple levels. We model the correlations between bitemporal semantic features using a TC module that enhances the identification of unchanged areas. For change detection, we design a semantic difference interaction module based on a graph convolutional network. It measures the interactions among bitemporal semantic features, their corresponding difference features, and the combination of both. Extensive experiments on four datasets, namely SECOND, HRSCD, Fuzhou, and Xiamen, show that HGINet performs better in identifying changed areas and categories across various scenarios and regions than nine existing methods. Compared with the existing methods applied on the four datasets, it achieves the highest F 1 scd values of 59.48%, 64.12%, 64.45%, and 84.93%, and SeK values of 19.34%, 14.55%, 18.28%, and 51.12%, respectively. Moreover, HGINet mitigates the influence of fake changes caused by seasonal effects, producing results with well-delineated boundaries and shapes. Furthermore, HGINet trained on the Fuzhou dataset is successfully transferred to the Xiamen dataset, demonstrating its effectiveness and robustness in identifying changed areas and categories from high-resolution remote sensing images. The code of our paper is accessible at https://github.com/long123524/HGINet-torch. [ABSTRACT FROM AUTHOR]

• ClearSCD considers the bidirectional dependency between semantics and change. • A BSCC mechanism establishes the correlation of bi-temporal semantic features. • A deep CVAPS module enhances the efficiency of change detection. • A SACL module optimizes feature space of semantics explicitly. • A new semantic change detection dataset, LsSCD, was proposed by this study. The Earth has been undergoing continuous anthropogenic and natural change. High spatial resolution (HSR) remote sensing imagery provides a unique opportunity to accurately reveal these changes on a planetary scale. Semantic change detection (SCD) with HSR imagery has become a common technique for tracking the evolution of land surface types at a semantic level. However, existing SCD methods rarely model the dependency between semantics and changes, resulting in suboptimal accuracy in detecting complicated surface changes. To address this limitation, we propose ClearSCD, a multi-task learning model that leverages the mutual gain relationship between semantics and change through three innovative modules. The first module interprets semantic features at different times into posterior probabilities for surface types to detect binary change information; the second module learns the correlation between surface types over time and the binary change information; a semantic augmented contrastive learning module is used as the third module to improve the performance of the other two modules. We tested ClearSCD's performance against state-of-the-art methods on benchmark datasets and a real-world scenario (named LsSCD dataset), showing that ClearSCD outperformed the alternatives on mIoU sc metrics by 1.23% to 19.34%. Furthermore, ablation experiments demonstrated the unique contribution of the three innovative modules to performance improvement. The high computational efficiency and robust performance over diverse landscapes demonstrate that ClearSCD is an operational tool for detecting detailed land surface changes from HSR imagery. Code and LsSCD dataset available at https://github.com/tangkai-RS/ClearSCD. [ABSTRACT FROM AUTHOR]

In recent years, numerous change detection methodologies have been proposed, with a predominant focus on binary change detection. Furthermore, there exists a paucity of research addressing semantic change detection in scenarios where solely binary change labels are available. This article introduces a multitask network for semantic change detection. First, 3-D ResUnet model is employed to generate initial multitemporal land cover results through postclassification comparison. Subsequently, the multitask network, encompassing two subtasks—binary change detection and multitemporal semantic segmentation—is proposed. Specifically, the shared branch of the network employs 3-D residual blocks to extract joint spectral-spatial features. In the subsequent task-specific branch, a 3-D GAN is incorporated for the binary change detection task to enhance the discrimination ability of latent high-level features for changes. Novel adaptive self-paced learning and certainty-weighted focal loss are proposed for multitemporal semantic segmentation to mitigate adverse effects from noisy semantic labels by considering sample complexity and reliability in the network optimization process. Experiments conducted on the Orbita Hyperspectral dataset in the Xiong'an New Area demonstrate the superior performance of the proposed method, achieving 99.28% and 76.60% for overall accuracy and kappa, respectively. This outperformance is notable when compared to other methods, such as Str4 and Bi-SRNet, showing an increase of 39.82% and 54.17% for kappa. Moreover, comparative experiments on SECOND further confirm the advantage of the proposed method, achieving 54.62% for kappa and outperforming other comparative methods, such as Bi-SRNet (47.61%).

The rapid advancement in Earth observation technologies has improved the acquisition of precise data on terrestrial changes. However, traditional binary change detection (BCD) fails to satisfy the complex demands of contemporary applications. In contrast, semantic change detection (SCD) can identify changed areas and further detect the types of changes in ground objects. Nonetheless, current SCD approaches pose significant challenges, including the inadequate extraction and utilization of multi-level features and limited synergistic integration of related tasks. This paper proposes a multi-level feature-aggregated network (MLFA-Net), which decomposes the SCD task into two related subtasks: ground object classification and BCD of bi-temporal images. To optimize the classification performance, MLFA-Net incorporates feature alignment and cross-level feature aggregation modules that operate between the encoders and decoders, achieving effective extraction, aggregation, and utilization of multi-level image features. MLFA-Net also integrates symmetric transform feature aggregation modules between classification and BCD branches, leveraging prior knowledge to facilitate cross-task transmission of multi-level information and further optimize SCD results. MLFA-Net achieved [Formula: see text] of 0.6214 and 0.3581 on Landsat-SCD and SECOND datasets, respectively, with improvements of at least 0.0402 and 0.0035 compared to other methods. Quantitative and qualitative experiments validate the efficacy and superiority of the proposed MLFA-Net in SCD tasks.

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.

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. [ABSTRACT FROM AUTHOR]

Remote sensing imagery allows temporal and large-scale observation of the Earth, and advanced techniques such as deep learning have been developed to deal with the massive data. As a result, by combining remote sensing with deep learning, it is possible to realize change detection, which can reveal the change in land cover and land use. Recent work has focused on semantic change detection (SCD), which simultaneously locates where the changes took place while identifying what objects changed between the different periods. Deep learning based SCD methods are typically based on a multi-task structure, which combines the classification and binary change detection (BCD). In this structure, the change region proposals are derived from the transformation (e.g., concatenation, differencing) of multi-temporal images, but after being processed by a convolutional neural network, there can be an incomplete response in the spatio-temporal features. Because of the high similarity of the color, texture, and geometric information of the ground objects in temporal high-resolution remote sensing imagery, this reduces the distinguishability of features and makes it difficult to identify changed areas. Meanwhile, there is less interaction between the BCD and classification, resulting in semantic inconsistency in changed areas. In this paper, the temporal-agnostic change region proposal network (TCRPN) is proposed, which is based on exploring the saliency of the changed regions in single-temporal images to highlight the changed areas in the spatio-temporal features and improve the response to change. As the changed areas can be the salient regions in the single-temporal images, a single-image salient area extractor (SSAE) is proposed to extract the salient areas from the different temporal images, obtaining salient change region candidates. As the change-related salient regions are temporally agnostic, temporal saliency fusion is used to integrate the candidates extracted from the single-temporal images to obtain a more reliable salient area map, where the salient area map is used to enhance the spatio-temporal features. Meanwhile, a shortcut is proposed to further make the change representation more distinguishable and suppress incorrectly highlighted regions. Directly utilizing multi-temporal images for change region proposal can reduce the change information loss and increase the interaction between the classification and BCD tasks to obtain better spatio-temporal features. The TCRPN is simple and flexible, and can be plugged into any multi-task SCD network. We selected four advanced multi-task SCD methods as the baseline, and added the TCRPN in their encoder or decoder part to conduct comprehensive experiments on three large-scale SCD datasets. The experimental results confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The utility of semantic change detection in myriad change scenarios has garnered considerable attention in contemporary research; however, its applicability in monitoring alterations in wetland ecosystems remains incompletely elucidated. To surmount the constraints associated with binary change detection methodologies—chiefly their insufficiency in the extraction of bi-temporal attributes—we introduced the Bi-Temporal Semantic Reasoning UNet++ (Bi-SRUNet++) algorithm. This algorithm leverages the architectural strengths of UNet++ as its foundational network to precisely delineate features pertinent to multi-class change detection. As a preliminary step, the study focused on the Dongting Lake wetland in China and conducted an analysis of feature trends predicated upon the monthly Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), as derived from Landsat 8 data spanning 2021–2022. Subsequently, the optimal temporal phases for change detection were ascertained through differential analyses between NDWI and NDVI metrics. Implementing the Bi-SRUNet++ algorithm on a pair of Sentinel-2 images, captured during the optimal phases, yielded augmented change information. Comparative evaluations reveal that the Bi-SRUNet++ algorithm, conceptualized on the framework of Bi-Temporal Semantic Reasoning Network (Bi-SRNet), surpasses the performance indices of its counterpart Semantic Segmentation and Change Detection Late Fusion (SSCD-l). Furthermore, the incorporation of the UNet++ backbone network amplifies the algorithm's capacity for semantic feature extraction, thereby enhancing the efficacy of Bi-SRUNet++ in wetland change detection. The analysis divulges that the total altered area of Dongting Lake during the 2021–2022 period amounts to 1187.97 km2, comprising a water loss of 1186.16 km2, a 715.34 km2 transformation into vegetation, and a conversion of 469.96 km2 into mudflats. The codes and partial dataset in this paper are available at: https://github.com/vivianmiumiu/Bi-SRUNetplusplus-for-SCD.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 dataset demonstrate that our SMNet obtains 71.95% and 20.29% at mean Intersection over Union (mIoU) and Separated Kappa coefficient (Sek), respectively, which proves the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, change detection has been one of the hot research topics within the field of remote sensing. Previous studies have concentrated on binary change detection (BCD), but it doesn’t meet the current needs. Therefore, semantic change detection (SCD) is also gradually developing, which focuses on determining the specific changed type while obtaining changed areas. In the paper, we propose a multi-task learning method (MTSCD-Net) for SCD task. The SCD task is decoupled into two related subtasks, semantic segmentation (SS) and BCD, then unifies them under the same framework. Multi-scale features are extracted using the Siamese semantic-aware encoder based on Swin Transformer, and the aggregation module is designed to combine features. Then, the change information extraction module is designed to enhance the capacity to express features by fully integrating the two-level difference features that are generated from fused features. Moreover, in the decoder stage, the spatial attention weight map is obtained using the features of the BCD subtask, which provides location prior information for the features of the SS subtask. It helps fully explore the correlation between the two subtasks. The two loss functions of subtasks are weighted to train MTSCD-Net. The comparative experiments results on two typical SCD datasets confirm the advantage of MTSCD-Net for SCD task. For the SeK index, MTSCD-Net achieves 3.96% and 20.57% on HRSCD and SECOND datasets, respectively. This outperforms other comparative methods such as Bi-SRNet (which achieves 4.86% and 1.47% higher on two datasets, respectively). The same is true for the Score metric. Moreover, the ablation experiment results confirm the effectiveness of key modules.

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.

With the acceleration of urban expansion, urban change detection (UCD), as a significant and effective approach, can provide the change information with respect to geospatial objects for dynamic urban analysis. In recent years, through the use of machine learning and artificial intelligence, change detection methods have gradually developed from the traditional pixel-based comparison methods in the 1980s to data-driven deep learning methods. Deep learning methods have huge advantages in the application of remote sensing big data, by virtue of their huge feature extraction and expression capabilities. Many change detection datasets have been released to meet the requirements of deep learning. However, the existing datasets suffer from three bottlenecks: (1) the volume of the datasets is small, which can easily cause overfitting; (2) most datasets have a spatial resolution of meters, making it difficult to detect changes in small objects because there are multi-scale objects in urban areas; and (3) most of the datasets have been designed for binary change detection (BCD), and lack semantic annotation, so that they cannot be used to obtain the direction of the change or to analyze the type of change, for further application in urban areas. Therefore, it is difficult to apply these datasets to detect large-scale urban semantic changes in complex environments. To address these issues, a large-scale ultra high resolution (0.1 m) UCD dataset for deep learning based BCD and semantic change detection (SCD) is introduced in this article, which is named the Hi-UCD dataset. We selected an area of 102 m2 in Tallinn, the capital of Estonia, as the study area. There are a total of 40800 pairs of 512 × 512 patches, nine types of land cover and 48 types of semantic change in the Hi-UCD dataset. We developed three metrics–binary consistency, change area consistency and no-change area consistency–to evaluate the semantic consistency of the SCD methods from different aspects. A comprehensive analysis and investigation is provided in this article after we benchmarked this dataset using deep learning methods for BCD and SCD. We also found that the unchanged samples can help distinguish the changed area, and HRNet used as the backbone to construct a multi-task model can perform well in the Hi-UCD dataset. Meanwhile, the visualization results obtained with the Hi-UCD test set, which is a large geographic area covering 54 km2, are shown to reflect the real-world urban application scenarios. The experimental results show that the Hi-UCD dataset is a challenging yet useful benchmark dataset, which can be used for analyzing large-scale refined urban changes. [ABSTRACT FROM AUTHOR]

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.

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.

Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic (labels/categories) details before and after the timelines are analyzed. Periodical land change analysis is used for many real time applications for valuation purposes. Majority of the research works are focused on Convolutional Neural Networks (CNN) which tries to analyze changes alone. Semantic information of changes appears to be missing, there by absence of communication between the different semantic timelines and changes detected over the region happens. To overcome this limitation, a CNN network is proposed incorporating the Resnet-34 pre-trained model on Fully Convolutional Network (FCN) blocks for exploring the temporal data of satellite images in different timelines and change map between these two timelines are analyzed. Further this model achieves better results by analyzing the semantic information between the timelines and based on localized information collected from skip connections which help in generating a better change map with the categories that might have changed over a land area across timelines. Proposed model effectively examines the semantic changes such as from-to changes on land over time period. The experimental results on SECOND (Semantic Change detectiON Dataset) indicates that the proposed model yields notable improvement in performance when it is compared with the existing approaches and this also improves the semantic segmentation task on images over different timelines and the changed areas of land area across timelines. [ABSTRACT FROM AUTHOR]

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.

Due to the abundant features of high spatial resolution (HSR) remote sensing images, change detection of these images is crucial to understanding the land-use and land-cover (LULC) changes. However, previous works mostly focus on traditional binary change detection without considering the semantic information of the change classes. The latest progress of deep learning (DL) shows its advantages in HSR remote sensing images change detection. However, due to the large number of parameter calculations, the DL network always requires a large quantity of labeled data. In addition, DL methods for change detection usually follow a patch-based learning framework, which considers only the local area and leads to a sample imbalance problem for semantic change detection. To address the above issues, we first proposed a Siamese global learning (Siam-GL) framework, which is a novel semantic change detction framework for HSR remote sensing images. In Siam-GL, the Siamese architecture with shared parameters is constructed to effectively extract the representative features of bi-temporal HSR remote sensing images. The global hierarchical (G-H) sampling mechanism is designed to address the imbalanced training sample problem with insufficient samples. Furthermore, the binary change mask is added between the encoder and decoder to weaken the influence of the no-change regional background on the change regional foreground, further improving the accuracy of the proposed framework. The experimental results obtained with three diverse HSR datasets of typical Chinese cities demonstrated that the Siam-GL framework outperforms the advanced semantic change detection methods in terms of both quantity and quality. Moreover, to verify the generalization performance of the Siam-GL framework, a larger dataset was used for evaluation, and the results show that the Siam-GL framework has strong generalization performance. [ABSTRACT FROM AUTHOR]

With the continuing improvement of remote-sensing (RS) sensors, it is crucial to monitor Earth surface changes at fine scale and in great detail. Thus, semantic change detection (SCD), which is capable of locating and identifying “from-to” change information simultaneously, is gaining growing attention in RS community. However, due to the limitation of large-scale SCD datasets, most existing SCD methods are focused on scene-level changes, where semantic change maps are generated with only coarse boundary or scarce category information. To address this issue, we propose a novel convolutional network for large-scale SCD (SCDNet). It is based on a Siamese UNet architecture, which consists of two encoders and two decoders with shared weights. First, multi-temporal images are given as input to the encoders to extract multi-scale deep representations. A multi-scale atrous convolution (MAC) unit is inserted at the end of the encoders to enlarge the receptive field as well as capturing multi-scale information. Then, difference feature maps are generated for each scale, which are combined with feature maps from the encoders to serve as inputs for the decoders. Attention mechanism and deep supervision strategy are further introduced to improve network performance. Finally, we utilize softmax layer to produce a semantic change map for each time image. Extensive experiments are carried out on two large-scale high-resolution SCD datasets, which demonstrates the effectiveness and superiority of the proposed method.

• Dongting Lake has experienced land covers changes due to extreme hydrologic events. • We use qualitative and quantitative methods for change detection in Dongting Lake. • A new change detection method Bi-SRUNet++ is proposed for change detection. • This method has the great potential to improve the accuracy of wetland change detection. • The obtained information is important for wetland management and sustainable development. The utility of semantic change detection in myriad change scenarios has garnered considerable attention in contemporary research; however, its applicability in monitoring alterations in wetland ecosystems remains incompletely elucidated. To surmount the constraints associated with binary change detection methodologies—chiefly their insufficiency in the extraction of bi-temporal attributes—we introduced the Bi-Temporal Semantic Reasoning UNet++ (Bi-SRUNet++) algorithm. This algorithm leverages the architectural strengths of UNet++ as its foundational network to precisely delineate features pertinent to multi-class change detection. As a preliminary step, the study focused on the Dongting Lake wetland in China and conducted an analysis of feature trends predicated upon the monthly Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), as derived from Landsat 8 data spanning 2021–2022. Subsequently, the optimal temporal phases for change detection were ascertained through differential analyses between NDWI and NDVI metrics. Implementing the Bi-SRUNet++ algorithm on a pair of Sentinel-2 images, captured during the optimal phases, yielded augmented change information. Comparative evaluations reveal that the Bi-SRUNet++ algorithm, conceptualized on the framework of Bi-Temporal Semantic Reasoning Network (Bi-SRNet), surpasses the performance indices of its counterpart Semantic Segmentation and Change Detection Late Fusion (SSCD-l). Furthermore, the incorporation of the UNet++ backbone network amplifies the algorithm's capacity for semantic feature extraction, thereby enhancing the efficacy of Bi-SRUNet++ in wetland change detection. The analysis divulges that the total altered area of Dongting Lake during the 2021–2022 period amounts to 1187.97 km2, comprising a water loss of 1186.16 km2, a 715.34 km2 transformation into vegetation, and a conversion of 469.96 km2 into mudflats. The codes and partial dataset in this paper are available at: https://github.com/vivianmiumiu/Bi-SRUNetplusplus-for-SCD. [ABSTRACT FROM AUTHOR]

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.

This paper presents a novel change detection network for identifying scene changes about pairs of aerial images. The aerial images are obtained with Unmanned Aerial Vehicles (UAVs), used to analyze "where and how" changes happened between pairs of images at different times. Even that aerial images could conveniently provide real time detailed information for land cover analysis, there still exist two challenging obstacles for coping with the change detection issue on aerial images. The one is that the paired aerial images captured at different times are roughly aligned due to the fact that the camera is mounted on a moving platform like UAVs. The other one is that season changes, light changes, and noise disturbance frequently happen, and they are useless or even impeditive in real applications. To conquer the problems, we propose a change detection networks named dual regions of interest networks to locate semantic change with object level, which could be easier coping with the above-mentioned compared with pixel-based methods. Moreover, we also introduce a pipeline to create a "Aerial change detection dataset" for the research of the change detection issues of aerial images analysis. Our evaluations on this benchmark dataset, "CDnet 2014 dataset", and "AICD 2012 dataset" demonstrate the good detection and localization performance. [ABSTRACT FROM AUTHOR]

Fire products derived from coarse (500 m to 1 km) spatial resolution satellite data have become an important source of information for the fire science and applications communities. There is however a demand for moderate (30 m) spatial resolution burned area data sets, systematically generated at regional to global scale, that to date has been only partially met. This paper presents a methodology to fuse multi-temporal Landsat Enhanced Thematic Mapper plus (ETM +) data with Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detections to map systematically burned areas at 30 m resolution. A multistage mapping approach is used with an initial per-pixel change detection based on spectral-rule based pre-classification of Landsat 30 m time series to identify candidate burned areas. The candidate burned area objects are then either retained or discarded by comparison with contemporaneous MODIS active fire detections. The methodology is demonstrated for 1.9 million km 2 over the Western United States using all the Landsat 7 ETM + data and Terra MODIS active fire detections acquired in 2002. Systematic evaluation conducted by per-pixel and per-burned area object comparison with the burned area perimeters provided by the USGS Monitoring Trends in Burn Severity (MTBS) project is presented, and shows a good agreement in the identification of burning patterns but a likely underestimation of the total area burned. Future research to refine and further test the methodology is discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Change detection is one of the main problems in remote sensing, and is essential to the accurate processing and understanding of the large scale Earth observation data available. Most of the recently proposed change detection methods bring deep learning to this context, but change detection labelled datasets which are openly available are still very scarce, which limits the methods that can be proposed and tested. In this paper we present the first large scale very high resolution semantic change detection dataset, which enables the usage of deep supervised learning methods for semantic change detection with very high resolution images. The dataset contains coregistered RGB image pairs, pixel-wise change information and land cover information. We then propose several supervised learning methods using fully convolutional neural networks to perform semantic change detection. Most notably, we present a network architecture that performs change detection and land cover mapping simultaneously, while using the predicted land cover information to help to predict changes. We also describe a sequential training scheme that allows this network to be trained without setting a hyperparameter that balances different loss functions and achieves the best overall results. • A large scale very high resolution semantic change detection dataset has been created. • FCNNs have been successfully used for semantic change detection. • A network for integrated change detection and land cover mapping is described. • A training scheme that avoids using hyperparameters for balancing loss functions. [ABSTRACT FROM AUTHOR]