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Research Article

Synergistic fusion network for landslide segmentation: the vital complementarity of geological information to remote sensing imagery

, , , , , & show all
Received 07 Apr 2024, Accepted 04 May 2024, Published online: 17 May 2024

References

  • Tsai F, Hwang J-H, Chen L-C, et al. Post-disaster assessment of landslides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis. Nat Hazards Earth Syst Sci. 2010;10(10):2179–2190. doi: 10.5194/nhess-10-2179-2010
  • Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Boston (MA), USA; 2015. p. 3431–3440.
  • Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18. Springer; 2015. p. 234–241.
  • Chen L-C, Zhu Y, Papandreou G, et al. Encoder–decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV); Munich, Germany; 2018. p. 801–818.
  • Zhang Z, Liu Q, Wang Y. Road extraction by deep residual u-net. IEEE Geosci Remote Sens Lett. 2018;15(5):749–753. doi: 10.1109/LGRS.2018.2802944
  • Fu J, Liu J, Tian H, et al. Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; Long Beach (CA), USA; 2019. p. 3146–3154.
  • Xie E, Wang W, Yu Z, et al. Segformer: simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst. 2021;34:12077–12090.
  • Sameen MI, Pradhan B. Landslide detection using residual networks and the fusion of spectral and topographic information. IEEE Access. 2019;7:114363–114373. doi: 10.1109/Access.6287639
  • Xin L-b, Han L, Li L-z. Landslide intelligent recognition based on multi-source data fusion. J Earth Sci Environ. 2023;45:920–928.
  • Jin Y, Li X, Zhu S, et al. Accurate landslide identification by multisource data fusion analysis with improved feature extraction backbone network. Geom Nat Hazards Risk. 2022;13(1):2313–2332. doi: 10.1080/19475705.2022.2116357
  • Zhang WK, Liu WJ, Sun X, et al. Multi-source features adaptation fusion network for semantic segmentation in high-resolution remote sensing images. J Image Graph. 2022;27:2516–2526.
  • He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Las Vegas (NV), USA; 2016. p. 770–778.
  • Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Salt Lake City (UT), USA; 2018. p. 7132–7141.
  • Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2021. p. 13713–13722.
  • Ghorbanzadeh O, Xu Y, Ghamisi P, et al. Landslide4sense: reference benchmark data and deep learning models for landslide detection; 2022.Available from: arXiv preprint arXiv:2206.00515.
  • Jha D, Smedsrud PH, Riegler MA, et al. Resunet++: an advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM). IEEE; 2019. p. 225–2255.
  • Wang J, Sun K, Cheng T, et al. Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell. 2020;43(10):3349–3364. doi: 10.1109/TPAMI.2020.2983686

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