AFSNet: attention-guided full-scale feature aggregation network for high-resolution remote sensing image change detection

Jiang, Ming; Zhang, Xinchang; Sun, Ying; Feng, Weiming; Gan, Qiao; Ruan, Yongjian

doi:10.1080/15481603.2022.2142626

Cited by 10 publications

(4 citation statements)

References 55 publications

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“…F contains richer details and edge information, while M and D contain more accurate contextual information. To fully integrate multiscale feature information [28], [29] and select the enhanced semantic information at the same location but at different stages, we first perform a cross-multiplication between S, M, and D to obtain F1, F2, and F3. Subsequently, the features derived from F1, F2, and F3 are condensed in the channel dimension to obtain W1, W2, and W3, transitioning their tensor structure from B×C×H×W to B×1×H×W.…”

Section: Fsia Modulementioning

confidence: 99%

Dual-Dimension Feature Interaction for Semantic Change Detection in Remote Sensing Images

Wang,

Jiang,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing semantic change detection (SCD) involves extracting information about changes in land cover/land use (LCLU) within the same area at different times. This issue is of crucial significance in many Earth observation tasks, such as precise urban planning and natural resource management. However, the current methods primarily focus on spatial feature extraction, lacking awareness of temporal features. Consequently, there are challenges in extracting change features, making distinguishing intraclass and interclass differences difficult. This also contributes to pseudo-change, posing challenges for SCD tasks. To overcome the limitations of existing methods, we present a dual-dimension feature interaction network (DFINet) for SCD. First, to enhance the assessment and perceptual abilities related to intraclass and interclass differences, we introduce a temporal difference feature enhancement (TDFE) module. This module comprehensively captures features from the temporal dimension. Then, to address the interrelation between multitemporal and multilevel features, we investigate the feature selection interaction (FSIA) and interaction attention modules (IAM), which enable multidimensional deep fusion and interaction of change features. This enhances the capacity for information transfer and integration among the features within multitemporal remote sensing images (RSIs). The experimental results demonstrate that compared to existing methods, the proposed architecture achieves a significant improvement in accuracy. Additionally, the design enhancements added to DFINet boost the practicality of remote sensing SCD, underscoring its substantial research value.

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Section: Fsia Modulementioning

confidence: 99%

Dual-Dimension Feature Interaction for Semantic Change Detection in Remote Sensing Images

Wang,

Jiang,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…AEDNet: an attention-based encoder-decoder network for urban water extraction from high spatial resolution remote sensing images extracting features from raw images through multiple convolutional layers, eliminating the need for intricate feature engineering and significantly improving efficiency [22]- [24]. Among them, semantic segmentation models based on convolutional neural networks (CNNs) can extract semantic features from images and associate them with specific class labels.…”

Section: Introductionmentioning

confidence: 99%

AEDNet: An Attention-Based Encoder–Decoder Network for Urban Water Extraction From High Spatial Resolution Remote Sensing Images

Song,

Rui,

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate water extraction from urban remote sensing images holds great significance in assisting the formulation of river and lake management policies and ensuring the sustainable development of urban water resources. However, urban high-resolution remote sensing images encompass complex spatial and semantic information, which leads to disparities between the extracted water body features based on local and global information, consequently affecting the accuracy of urban water extraction. To tackle this issue, an attention-based encoder-decoder network (AEDNet) was proposed. In this network, the backbone employing atrous convolution facilitated the acquisition of low-level and high-level features of urban remote sensing images at various scales. Integrated with the attention mechanism, the encoderdecoder structure extracted global features in both the spatial and channel domains. Subsequently, these two types of features were merged to yield the urban water segmentation. Moreover, considering both intersection over union (IoU) and class weights, a joint loss function was introduced to further enhance the accuracy of urban water extraction. Experimental results demonstrated the strong performance of the proposed method on both GID and LoveDA datasets.

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“…Land cover change detection plays a crucial role in understanding dynamics on the Earth's surface, with it being indispensable for applications such as land use analysis, environmental assessment, monitoring of human development, and disaster response [1][2][3][4][5]. The increasing availability of high-resolution remote sensing (RS) images has revolutionized the field of change detection, enabling detailed long-term monitoring of land cover dynamics [6].…”

Section: Introductionmentioning

confidence: 99%

Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network

Wang,

Zhu,

Zheng

et al. 2024

Remote Sensing

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Vector polygons represent crucial survey data, serving as a cornerstone of national geographic censuses and forming essential data sources for detecting geographical changes. The timely update of these polygons is vital for governmental decision making and various industrial applications. However, the manual intervention required to update existing vector polygons using up-to-date high-resolution remote sensing (RS) images poses significant challenges and incurs substantial costs. To address this, we propose a novel change detection (CD) method for land cover vector polygons leveraging high-resolution RS images and deep learning techniques. Our approach begins by employing the boundary-preserved masking Simple Linear Iterative Clustering (SLIC) algorithm to segment RS images. Subsequently, an adaptive cropping approach automatically generates an initial sample set, followed by denoising using the efficient Visual Transformer and Class-Constrained Density Peak-Based (EViTCC-DP) method, resulting in a refined training set. Finally, an enhanced attention-based multi-scale ConvTransformer network (AMCT-Net) conducts fine-grained scene classification, integrating change rules and post-processing methods to identify changed vector polygons. Notably, our method stands out by employing an unsupervised approach to denoise the sample set, effectively transforming noisy samples into representative ones without requiring manual labeling, thus ensuring high automation. Experimental results on real datasets demonstrate significant improvements in model accuracy, with accuracy and recall rates reaching 92.08% and 91.34%, respectively, for the Nantong dataset, and 93.51% and 92.92%, respectively, for the Guantan dataset. Moreover, our approach shows great potential in updating existing vector data while effectively mitigating the high costs associated with acquiring training samples.

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AFSNet: attention-guided full-scale feature aggregation network for high-resolution remote sensing image change detection

Cited by 10 publications

References 55 publications

Dual-Dimension Feature Interaction for Semantic Change Detection in Remote Sensing Images

Dual-Dimension Feature Interaction for Semantic Change Detection in Remote Sensing Images

AEDNet: An Attention-Based Encoder–Decoder Network for Urban Water Extraction From High Spatial Resolution Remote Sensing Images

Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network

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