DCI-PGCN: Dual-Channel Interaction Portable Graph Convolutional Network for Landslide Detection

Fu, Yibing; Fan, Shuaishuai; Mingrui, Xin; Bai, Hongyang

doi:10.1109/tgrs.2023.3273623

Cited by 11 publications

(1 citation statement)

References 44 publications

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“…Graph convolutional neural networks (GCNs) address the above issue by propagating information through multiple layers of graph convolutions to some extent, resolving the long-distance connectivity of road nodes. [12][13][14] GCNs can effectively handle the incompleteness and discontinuity in road segmentation from high-resolution remote sensing images. [15][16][17] Despite this, the application of GCNs in road segmentation is limited by their high computational demands and the requirement for larger receptive fields and global information modeling in high-resolution remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

DELFormer: detail-enhanced lightweight transformer for road segmentation

Xin,

Fu,

et al. 2023

J. Appl. Rem. Sens.

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.The road segmentation task has become increasingly important in fields such as urban planning, traffic management, and environmental monitoring. However, most existing deep learning-based methods suffer from issues such as poor temporal effectiveness and connectivity, making it a significant challenge to achieve high-precision and high-efficiency road segmentation. We propose a road segmentation model based on a detail-enhanced lightweight transformer. Through the connectivity enhancement module, the issue of spatial information loss is addressed, enhancing the modeling capability of the road network connectivity. The model incorporates a detail-enhancement strategy to capture the relationship between roads and the environment, enhancing the perception and expression of details while maintaining low computational complexity. Furthermore, the use of a lightweight multiple feature fusion module promotes information fusion from features at different scales while a maintaining lightweight design. Extensive experiments on two publicly available datasets demonstrate that our method achieves the best performance in terms of real-time effectiveness and accuracy.

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