Abstract
Landslides are hazardous and in many cases can cause enormous economic losses and human casualties. The suddenness of landslides makes it difficult to detect landslides quickly and effectively. Therefore, to address the problem of intelligent analysis of geological landslides, we propose a 4RATFNet network for few-shot semantic segmentation detection in the case of insufficient number of labeled landslide images. First, a residual-attention module is designed to fuse channel features and spatial features for residual fusion. Second, improved transfer learning is used to optimize the parameters of the pre-trained network. Third, the network downscales the four-dimensional convolutional kernel into a pair of two-dimensional convolutional kernels. Finally, the few-shot semantic segmentation network is used to extract support image features and complete the landslide detection for the same features in the query image. The experimental results show that the method performs better when tested on Resnet50 backbone and Resnet101 backbone when the sample size of labeled landslide images is insufficient. Compared with traditional semantic segmentation methods, it can obtain better segmentation results and achieve higher mean intersection over union, indicating that our network has obvious advantages and wider applicability.
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References
Wang, X., Fan, X., Xu, Q., Du, P.: Change detection-based co-seismic landslide mapping through extended morphological profiles and ensemble strategy. ISPRS J. Photogramm. Remote. Sens. 187, 225–239 (2022)
Ghorbanzadeh, O., Shahabi, H., Crivellari, A., Homayouni, S., Blaschke, T., Ghamisi, P.: Landslide detection using deep learning and object-based image analysis. Landslides 19(4), 929–939 (2022)
Feizizadeh, B., Ghorbanzadeh, O.: GIS-based interval pairwise comparison matrices as a novel approach for optimizing an analytical hierarchy process and multiple criteria weighting. GI_Forum 1, 27–35 (2017)
Ghorbanzadeh, O., Blaschke, T., Aryal, J., Gholaminia, K.: A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. J. Spat. Sci. 65(3), 401–418 (2020)
Tian, Y., et al.: A transformer-based model for short-term landslide displacement prediction. Acta Sci. Naturalium Univ. Pekinensis 59(2), 197–210 (2023)
Ding, X., Zhao, X., Wu, X., Zhang, T., Xu, Z.: Landslide susceptibility assessment model based on multi-class SVM with RBF kernel. China Saf. Sci. J. 32(3), 194–200 (2022)
Huang, Y., Shi, P., He, H., He, H., Zhao, B.: Senet: spatial information enhancement for semantic segmentation neural networks. Vis. Comput. 1–14 (2023)
Ma, Z., Yuan, M., Gu, J., Meng, W., Xu, S., Zhang, X.: Triple-strip attention mechanism-based natural disaster images classification and segmentation. Vis. Comput. 38(9–10), 3163–3173 (2022)
Qin, Y., Chi, X., Sheng, B., Lau, R.W.: GuideRender: large-scale scene navigation based on multi-modal view frustum movement prediction. Vis. Comput. 1–11 (2023)
Yang, P., Wang, M., Yuan, H., He, C., Cong, L.: Using contour loss constraining residual attention U-net on optical remote sensing interpretation. Vis. Comput. 1–13 (2022)
Chen, Q., Yang, Y., Huang, T., Feng, Y.: A survey on few-shot image semantic segmentation. Front. Data Comput. 3(6), 17–34 (2021)
Rakelly, K., Shelhamer, E., Darrell, T., Efros, A., Levine, S.: Conditional networks for few-shot semantic segmentation. In: International Conference on Learning Representations (2018)
Shaban, A., Bansal, S., Liu, Z., Essa, I., Boots, B.: One-shot learning for semantic segmentation. In: British Machine Vision Conference 2017, pp. 167.1–167.13 (2017)
Zhao, Y., Price, B., Cohen, S., Gurari, D.: Objectness-aware few-shot semantic segmentation. arXiv preprint arXiv:2004.02945 (2020)
Min, J., Kang, D., Cho, M.: Hypercorrelation squeeze for few-shot segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6941–6952 (2021)
Fan, Q., Pei, W., Tai, Y.W., Tang, C.K.: Self-support few-shot semantic segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XIX. LNCS, vol. 13679, pp. 701–719. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19800-7_41
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Li, X., Wei, T., Chen, Y.P., Tai, Y.W., Tang, C.K.: FSS-1000: a 1000-class dataset for few-shot segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2869–2878 (2020)
Zeng, C., Cao, Z., Su, F., Zeng, Z., Yu, C.: High-precision aerial imagery and interpretation dataset of landslide and debris flow disaster in Sichuan and surrounding areas. China Sci. Data 7(2), 195–205 (2022)
Nguyen, K., Todorovic, S.: Feature weighting and boosting for few-shot segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 622–631 (2019)
Jiang, W., et al.: Deep learning for landslide detection and segmentation in high-resolution optical images along the Sichuan-Tibet transportation corridor. Remote Sens. 14(21), 5490 (2022)
Chen, Y., et al.: Susceptibility-guided landslide detection using fully convolutional neural network. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 16, 998–1018 (2022)
Acknowledgements
This work was supported by the Sichuan Science and Technology Program under Grant No. 2022YFG0148 and the Heilongjiang Science and Technology Program under Grant No. 2022ZX01A16.
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Huang, S., Li, Q., Li, J., Lu, J. (2024). 4RATFNet: Four-Dimensional Residual-Attention Improved-Transfer Few-Shot Semantic Segmentation Network for Landslide Detection. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14497. Springer, Cham. https://doi.org/10.1007/978-3-031-50075-6_6
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