MSTDSNet-CD: Multiscale Swin Transformer and Deeply Supervised Network for Change Detection of the Fast-Growing Urban Regions

Song, Fei; Zhang, Sanxing; Leí, Tao; Song, Yixuan; Peng, Zhenming

doi:10.1109/lgrs.2022.3165885

Cited by 37 publications

(19 citation statements)

References 13 publications

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“…Supervised learning methods for detecting land cover change require a large amount of labeled data and are widely utilized in the field of LCCD. Various algorithms have been proposed by researchers to address different challenges in this domain, and these methods are primarily categorized into two types: pixel-based and object-based approaches [64][65][66][67][68][69]. This section reviews and discusses in depth the existing methods of these two classes.…”

Section: Supervised Learning Methodsmentioning

confidence: 99%

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Gu,

Zeng

2023

Sustainability

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The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain.

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Section: Supervised Learning Methodsmentioning

confidence: 99%

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Gu,

Zeng

2023

Sustainability

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“…In order to extract the depth features of dual-phase images and their high-frequency differences, this paper designs a 3branch network framework based on Deeplabv3, and replaces the CNN network with Swin Transformer structure [8] , which has stronger feature extraction capability of global receptor field. The Transformer structure originates from the field of natural language processing.…”

Section: Depth Feature Extraction Modulementioning

confidence: 99%

Remote sensing image small target and moving target change detection method

Du,

Liu,

Zhang

et al. 2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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Multi-temporal remote sensing image change detection is an important research direction in the field of remote sensing, which has a wide range of applications in environmental monitoring, urban change, agricultural investigation, war symptoms prediction, military force deployment, military strike damage assessment and so on. In order to solve the difficult problem of change detection of some moving objects and small objects in remote sensing images, it is necessary to mine the global complex semantic information in the feature extraction stage, and the current research on this aspect is not in-depth. In this paper, a 3-branch TSPNet network based on Transformer structure and SKAttention mechanism is proposed. This model has advantages of high detection accuracy, full mining of global semantic information and high degree of refinement, which can realize fine detection and moving target detection. The model was tested on the CDD and Levi-CD datasets, and parameters such as F1 scores and mIoU outperformed other mainstream networks.

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“…Inspired by this, ViT [16] pioneered the introduction of the transformer architecture to large-scale image recognition with great success. Researchers have progressively applied it to change detection tasks [17][18][19][20]. Self-attention is the core component of the transformer architecture, which explicitly models one-dimensional sequence relations.…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods inadequately explore the attention mechanism between bi-temporal images in the CD task. The selfattention mechanism of [17][18][19][20] only models the non-local structural relations within a single temporal phase in Figure 1a and indiscriminately weights combinations of features in both changed and unchanged regions in the same way while ignoring the non-local structural relationships between the dual-temporal images in Figure 1b,c. Figure 1 illustrates the non-local structural relationships within the images, with the first row representing the "pre-change" image and the second row representing the "post-change" image.…”

Section: Introductionmentioning

confidence: 99%

DTT-CGINet: A Dual Temporal Transformer Network with Multi-Scale Contour-Guided Graph Interaction for Change Detection

Chen,

Jiang,

Zhou

2024

Remote Sensing

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Deep learning has dramatically enhanced remote sensing change detection. However, existing neural network models often face challenges like false positives and missed detections due to factors like lighting changes, scale differences, and noise interruptions. Additionally, change detection results often fail to capture target contours accurately. To address these issues, we propose a novel transformer-based hybrid network. In this study, we analyze the structural relationship in bi-temporal images and introduce a cross-attention-based transformer to model this relationship. First, we use a tokenizer to express the high-level features of the bi-temporal image into several semantic tokens. Then, we use a dual temporal transformer (DTT) encoder to capture dense spatiotemporal contextual relationships among the tokens. The features extracted at the coarse scale are refined into finer details through the DTT decoder. Concurrently, we input the backbone’s low-level features into a contour-guided graph interaction module (CGIM) that utilizes joint attention to capture semantic relationships between object regions and the contour. Then, we use the feature pyramid decoder to integrate the multi-scale outputs of the CGIM. The convolutional block attention modules (CBAMs) employ channel and spatial attention to reweight feature maps. Finally, the classifier discriminates change pixels and generates the final change map of the difference feature map. Several experiments have demonstrated that our model shows significant advantages over other methods in terms of efficiency, accuracy, and visual effects.

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MSTDSNet-CD: Multiscale Swin Transformer and Deeply Supervised Network for Change Detection of the Fast-Growing Urban Regions

Cited by 37 publications

References 13 publications

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Remote sensing image small target and moving target change detection method

DTT-CGINet: A Dual Temporal Transformer Network with Multi-Scale Contour-Guided Graph Interaction for Change Detection

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