MFGAN: multi feature guided aggregation network for remote sensing image

Chu, Shengguang; Xia, Min

doi:10.1007/s00521-022-06999-8

Cited by 14 publications

(7 citation statements)

References 44 publications

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“…With the wide attention of remote sensing image CD in the field of image processing, many high-quality algorithms based on CNNs have been applied to this task in recent years [42,43]. The task of remote sensing image CD is essentially a two-class semantic segmentation process, and CNNs perfectly meet this task.…”

Section: Methodsmentioning

confidence: 99%

“…In this section, we conduct a comprehensive comparison between our proposed model ABMFNet and a range of existing change detection models, including traditional ones such as FC_EF, FC-Siam-Diff, FC-Siam-Conc [60], SNUNet [61], ChangeNet [62], TCDNet [63], DASNet [64], MFGAN [43], TFI-GR [65], and SAGNet [38], as well as deep learning models like FCN8s [33], SegNet [66], UNet [32], HRNet [55], DeepLabV3+ [67], and BiseNet [68]. We evaluate these models based on several criteria, including their complexity and parameter count, as well as F1 score and MIoU values.…”

Section: Model Performance Comparisonmentioning

confidence: 99%

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Multi-Scale Fusion Siamese Network Based on Three-Branch Attention Mechanism for High-Resolution Remote Sensing Image Change Detection

Li,

Weng,

Xia

et al. 2024

Remote Sensing

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Remote sensing image change detection (CD) is an important means in remote sensing data analysis tasks, which can help us understand the surface changes in high-resolution (HR) remote sensing images. Traditional pixel-based and object-based methods are only suitable for low- and medium-resolution images, and are still challenging for complex texture features and detailed image detail processing in HR images. At present, the method based on deep learning has problems such as inconsistent fusion and difficult model training in the combination of the difference feature information of the deep and shallow layers and the attention mechanism, which leads to errors in the distinction between the changing region and the invariant region, edge detection and small target detection. In order to solve the above problems of inconsistent fusions of feature information aggregation and attention mechanisms, and indistinguishable change areas, we propose a multi-scale feature fusion Siamese network based on attention mechanism (ABMFNet). To tackle the issues of inconsistent fusion and alignment difficulties when integrating multi-scale fusion and attention mechanisms, we introduce the attention-based multi-scale feature fusion module (AMFFM). This module not only addresses insufficient feature fusion and connection between different-scale feature layers, but also enables the model to automatically learn and prioritize important features or regions in the image. Additionally, we design the cross-scale fusion module (CFM) and the difference feature enhancement pyramid structure (DEFPN) to assist the AMFFM module in integrating differential information effectively. These modules bridge the spatial disparity between low-level and high-level features, ensuring efficient connection and fusion of spatial difference information. Furthermore, we enhance the representation and inference speed of the feature pyramid by incorporating a feature enhancement module (FEM) into DEFPN. Finally, the BICD dataset proposed by the laboratory and public datasets LEVIR-CD and BCDD are compared and tested. We use F1 score and MIoU values as evaluation metrics. For AMBMFNet, the F1 scores on the three datasets are 77.69%, 81.57%, and 77.91%, respectively, while the MIoU values are 84.65%, 85.84%, and 84.54%, respectively. The experimental results show that ABMFNet has better effectiveness and robustness.

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Section: Methodsmentioning

confidence: 99%

Section: Model Performance Comparisonmentioning

confidence: 99%

Multi-Scale Fusion Siamese Network Based on Three-Branch Attention Mechanism for High-Resolution Remote Sensing Image Change Detection

Li,

Weng,

Xia

et al. 2024

Remote Sensing

Self Cite

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“…With the advancement of deep learning technology, it has been widely applied to remote sensing-based investigations [21][22][23][24][25]. For example, Kussul et al [26] used unsupervised neural networks to classify land cover and crop types using multi-temporal and multi-source satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

Robust Land Cover Classification With Local–Global Information Decoupling to Address Remote Sensing Anomalous Data

Xiao,

Cheng,

Chen

et al. 2024

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

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Remote sensing images play a critical role in urban planning, land resources and environmental monitoring. Land cover classification is one of the straightforward applications of remote sensing. However, the anomalous remote sensing data challenges the reliability of land cover classification results. Deep learning has been widely used in remote sensing image analysis, but it remains sensitive to anomalous data. To address this issue, we re-evaluate a land cover classification map in high-noise scenarios with anomalous data and propose a novel network architecture to solve the problem. A new network architecture is proposed to solve this problem. Our proposed network architecture focuses on decoupling the extraction of global information and local information. Through three global-local feature fusion modules, we output features emphasizing global information, features emphasizing local information, and consistency evaluation scores, respectively. A specially designed decoder integrates these three features. Our method performs better compared to mainstream models on the public datasets WHDLD with obvious anomaly data, with a MIoU of 63.58 % and a Mpa of 74.32 %. Compared to the suboptimal method, our method improves MIoU by 1.29 % and Mpa by 3.05 %.

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“…The advancement of computer science and sensor technology has led to the extensive use of remote sensing in various fields, such as environmental science, ecology, and urban planning [1][2][3][4][5][6]. Obtaining spatial and attribute information from remote sensing images plays a crucial role in the progress of remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Fast Semantic Segmentation of Remote Sensing Images Using a Network That Integrates Global and Local Information

Cui

et al. 2023

Sensors

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Efficient processing of ultra-high-resolution images is increasingly sought after with the continuous advancement of photography and sensor technology. However, the semantic segmentation of remote sensing images lacks a satisfactory solution to optimize GPU memory utilization and the feature extraction speed. To tackle this challenge, Chen et al. introduced GLNet, a network designed to strike a better balance between GPU memory usage and segmentation accuracy when processing high-resolution images. Building upon GLNet and PFNet, our proposed method, Fast-GLNet, further enhances the feature fusion and segmentation processes. It incorporates the double feature pyramid aggregation (DFPA) module and IFS module for local and global branches, respectively, resulting in superior feature maps and optimized segmentation speed. Extensive experimentation demonstrates that Fast-GLNet achieves faster semantic segmentation while maintaining segmentation quality. Additionally, it effectively optimizes GPU memory utilization. For example, compared to GLNet, Fast-GLNet’s mIoU on the Deepglobe dataset increased from 71.6% to 72.1%, and GPU memory usage decreased from 1865 MB to 1639 MB. Notably, Fast-GLNet surpasses existing general-purpose methods, offering a superior trade-off between speed and accuracy in semantic segmentation.

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MFGAN: multi feature guided aggregation network for remote sensing image

Cited by 14 publications

References 44 publications

Multi-Scale Fusion Siamese Network Based on Three-Branch Attention Mechanism for High-Resolution Remote Sensing Image Change Detection

Multi-Scale Fusion Siamese Network Based on Three-Branch Attention Mechanism for High-Resolution Remote Sensing Image Change Detection

Robust Land Cover Classification With Local–Global Information Decoupling to Address Remote Sensing Anomalous Data

Fast Semantic Segmentation of Remote Sensing Images Using a Network That Integrates Global and Local Information

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