Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Cui, Wei; Meng, Yan; Hao, Yuanjie; Wang, Ziwei; He, Xin; Wu, Weijie; Li, Jie; Zhao, Huilin; Xia, Cong; Wang, Jin

doi:10.3390/s21113848

Cited by 15 publications

(4 citation statements)

References 40 publications

(57 reference statements)

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“…Unsupervised classification, also known as cluster analysis or group point analysis, requires no prior data and minimal initial human input. This method ignores the spatial information of the image, and the computer automatically classifies similar characters into one category according to certain rules based on differences in the data themselves, which may lead to the phenomenon of "different objects have the same spectrum" [16], resulting in the incorrect classification of water bodies and other features.…”

Section: Introductionmentioning

confidence: 99%

A Method for Extracting Lake Water Using ViTenc-UNet: Taking Typical Lakes on the Qinghai-Tibet Plateau as Examples

et al. 2023

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As the lakes located in the Qinghai-Tibet Plateau are important carriers of water resources in Asia, dynamic changes to these lakes intuitively reflect the climate and water resource variations of the Qinghai-Tibet Plateau. To address the insufficient performance of the Convolutional Neural Network (CNN) in learning the spatial relationship between long-distance continuous pixels, this study proposes a water recognition model for lakes on the Qinghai-Tibet Plateau based on U-Net and ViTenc-UNet. This method uses Vision Transformer (ViT) to replace the continuous Convolutional Neural Network layer in the encoder of the U-Net model, which can more accurately identify and extract the continuous spatial relationship of lake water bodies. A Convolutional Block Attention Module (CBAM) mechanism was added to the decoder of the model enabling the spatial information and spectral information characteristics of the water bodies to be more completely preserved. The experimental results show that the ViTenc-UNet model can complete the task of lake water recognition on the Qinghai-Tibet Plateau more efficiently, and the Overall Accuracy, Intersection over Union, Recall, Precision, and F1 score of the classification results for lake water bodies reached 99.04%, 98.68%, 99.08%, 98.59%, and 98.75%, which were, respectively, 4.16%, 6.20% 5.34%, 4.80%, and 5.34% higher than the original U-Net model. Compared to FCN, the DeepLabv3+, TransUNet, and Swin-Unet models also have different degrees of advantages. This model innovatively introduces ViT and CBAM into the water extraction task of lakes on the Qinghai-Tibet Plateau, showing excellent water classification performance of these lake bodies. This method has certain classification advantages and will provide an important scientific reference for the accurate real-time monitoring of important water resources on the Qinghai-Tibet Plateau.

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Section: Introductionmentioning

confidence: 99%

A Method for Extracting Lake Water Using ViTenc-UNet: Taking Typical Lakes on the Qinghai-Tibet Plateau as Examples

et al. 2023

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“…Combining PCA, the attention mechanism, and U-Net, PSE-UNet [22] analyzes the factors affecting the performance, which makes it superior to other semantic segmentation algorithms. KG-GCN [23] uses superpixel blocks as graph network nodes, combines prior knowledge with spatial correlation in the process of information aggregation, and effectively overcomes the distortion of sample context. MCSGNet is a semantic segmentation network for classifying land-use types in remote sensing images.…”

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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“…At present, semantic segmentation of point clouds has emerged as a highly significant research topic across various domains, including machine vision, artificial intelligence, photogrammetry, remote sensing, etc. [1][2][3][4]. Three-dimensional semantic segmentation is one of the crucial areas within the field.…”

Section: Introductionmentioning

confidence: 99%

FGCN: Image-Fused Point Cloud Semantic Segmentation with Fusion Graph Convolutional Network

Zhang,

Chen,

Peng

et al. 2023

Sensors

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In interpreting a scene for numerous applications, including autonomous driving and robotic navigation, semantic segmentation is crucial. Compared to single-modal data, multi-modal data allow us to extract a richer set of features, which is the benefit of improving segmentation accuracy and effect. We propose a point cloud semantic segmentation method, and a fusion graph convolutional network (FGCN) which extracts the semantic information of each point involved in the two-modal data of images and point clouds. The two-channel k-nearest neighbors (KNN) module of the FGCN was created to address the issue of the feature extraction’s poor efficiency by utilizing picture data. Notably, the FGCN utilizes the spatial attention mechanism to better distinguish more important features and fuses multi-scale features to enhance the generalization capability of the network and increase the accuracy of the semantic segmentation. In the experiment, a self-made semantic segmentation KITTI (SSKIT) dataset was made for the fusion effect. The mean intersection over union (MIoU) of the SSKIT can reach 88.06%. As well as the public datasets, the S3DIS showed that our method can enhance data features and outperform other methods: the MIoU of the S3DIS can reach up to 78.55%. The segmentation accuracy is significantly improved compared with the existing methods, which verifies the effectiveness of the improved algorithms.

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Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Cited by 15 publications

References 40 publications

A Method for Extracting Lake Water Using ViTenc-UNet: Taking Typical Lakes on the Qinghai-Tibet Plateau as Examples

A Method for Extracting Lake Water Using ViTenc-UNet: Taking Typical Lakes on the Qinghai-Tibet Plateau as Examples

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

FGCN: Image-Fused Point Cloud Semantic Segmentation with Fusion Graph Convolutional Network

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