Abstract
With the rapid development of Geological Environment Remote Sensing (GERS) technology, accurately interpreting geological elements has become a critical task in the fields of geology and environmental science. To address the issue of low model interpretation accuracy caused by intra-class variation, inter-class similarity, and complex distribution in GERS, a new Multi-Scale Spatial and Channel Fusion Network MSCFNet, which consists of the Fine-grained Local feature Fusion (FLF) module, Multi-resolution Geological Context-Aware (MGCA) module, and Global Feature Aggregation (GFA) module, are proposed. A series of experiments on the GERS dataset of Northwest China have demonstrated the significant advantages of our approach. Compared with the mainstream semantic segmentation model, it has improved mPA by 3.1% and mIoU by 3.32%. Additionally, ablation experiments are performed to verify the performance enhancement of each module.
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References
Han, W., et al.: A survey of machine learning and deep learning in remote sensing of geological environment: challenges, advances, and opportunities. ISPRS J. Photogramm. Remote. Sens. 202, 87–113 (2023)
Sikakwe, G.U.: Mineral exploration employing drones, contemporary geological satellite remote sensing and geographical information system (gis) procedures: A review. Remote Sensing Applications: Society and Environment, p. 100988 (2023)
Wu, X., et al.: Analysis of geological hazard susceptibility of landslides in muli county based on random forest algorithm. Sustainability 15(5), 4328 (2023)
Geiß, C., Rabuske, A., Pelizari, P.A., Bauer, S., Taubenböck, H.: Selection of unlabeled source domains for domain adaptation in remote sensing. Array 15, 100233 (2022)
Mumuni, A., Mumuni, F.: Data augmentation: a comprehensive survey of modern approaches. Array 16, 100258 (2022)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Huang, Q., Fan, J., Xu, H., Han, W., Huang, X., Chen, Y.: Afenet: attention-guided feature enhancement network and a benchmark for low-altitude uav sewage outfall detection. Array 22, 100343 (2024)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Wang, S., Han, W., Huang, X., Zhang, X., Wang, L., Li, J.: Trustworthy remote sensing interpretation: concepts, technologies, and applications. ISPRS J. Photogramm. Remote. Sens. 209, 150–172 (2024)
Xu, D., Chen, Y., Cui, N., Li, J.: Towards multi-dimensional knowledge-aware approach for effective community detection in lbsn. World Wide Web 26(4), 1435–1458 (2023)
Haldar, N.A.H., et al.: Top-k socio-spatial co-engaged location selection for social users. IEEE Trans. Knowl. Data Eng. 35(5), 5325–5340 (2022)
Tang, Y., Li, J., Haldar, N.A.H., Guan, Z., Xu, J., Liu, C.: Reliability-driven local community search in dynamic networks. IEEE Trans. Knowl. Data Eng. (2023)
Sharma, V., Tripathi, A.K.: A systematic review of meta-heuristic algorithms in IoT based application. Array 14, 100164 (2022)
Wang, S., Han, W., Zhang, X., Li, J., Wang, L.: Geospatial remote sensing interpretation: From perception to cognition. The Innovation Geoscience 2(1), 100056–1 (2024)
Han, W., Li, J., Wang, S., Zhang, X., Dong, Y., Fan, R., Zhang, X., Wang, L.: Geological remote sensing interpretation using deep learning feature and an adaptive multisource data fusion network. IEEE Trans. Geosci. Remote. Sensing 60, 1–14 (2022)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Peterson, L.E.: K-nearest neighbor. Scholarpedia 4(2), 1883 (2009)
Maulik, U., Chakraborty, D.: Remote sensing image classification: a survey of support-vector-machine-based advanced techniques. IEEE Geosci. Remote Sensing Magazine 5(1), 33–52 (2017)
Abu El-Magd, S.A., Ali, S.A., Pham, Q.B.: Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and k-nearest neighbor in a complicated terrain. Earth Sci. Inf. 14(3), 1227–1243 (2021)
Feyisa, G.L., Meilby, H., Fensholt, R., Proud, S.R.: Automated water extraction index: a new technique for surface water mapping using landsat imagery. Remote Sens. Environ. 140, 23–35 (2014)
Sameen, M.I., Pradhan, B.: Landslide detection using residual networks and the fusion of spectral and topographic information. IEEE Access 7, 114363–114373 (2019)
Yuan, X., Shi, J., Gu, L.: A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Syst. Appl. 169, 114417 (2021)
Liu, J., Ban, W., Chen, Y., Sun, Y., Zhuang, H., Fu, E., Zhang, K.: Multi-dimensional CNN fused algorithm for hyperspectral remote sensing image classification. Zhongguo Jiguang/Chinese J. Lasers 48(16), 1–11 (2021)
Hamedianfar, A., Mohamedou, C., Kangas, A., Vauhkonen, J.: Deep learning for forest inventory and planning: a critical review on the remote sensing approaches so far and prospects for further applications. Forestry 95(4), 451–465 (2022)
Zhang, J., Lin, S., Ding, L., Bruzzone, L.: Multi-scale context aggregation for semantic segmentation of remote sensing images. Remote Sens. 12(4), 701 (2020)
Wang, S., Huang, X., Han, W., Li, J., Zhang, X., Wang, L.: Lithological mapping of geological remote sensing via adversarial semi-supervised segmentation network. Int. J. Appl. Earth Obs. Geoinf. 125, 103536 (2023)
Yang, J., Wu, C., Du, B., Zhang, L.: Enhanced multiscale feature fusion network for HSI classification. IEEE Trans. Geosci. Remote Sens. 59(12), 10328–10347 (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Comput. Vis. Pattern Recognition, pp. 770–778 (2016)
Yuan, Y., Huang, L., Guo, J., Zhang, C., Chen, X., Wang, J.: Ocnet: object context for semantic segmentation. Int. J. Comput. Vis. 129(8), 2375–2398 (2021)
Liu, Y., Shao, Z., Hoffmann, N.: Global attention mechanism: retain information to enhance channel-spatial interactions. arXiv preprint arXiv:2112.05561 (2021)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Comput. Vis. Pattern Recognition, pp. 3431–3440 (2015)
Florian, L.C., Adam, S.H.: Rethinking atrous convolution for semantic image segmentation. In: IEEE Comput. Vis. Pattern Recognition, vol. 6 (2017)
Wu, T., Tang, S., Zhang, R., Cao, J., Zhang, Y.: Cgnet: a light-weight context guided network for semantic segmentation. IEEE Trans. Image Process. 30, 1169–1179 (2020)
Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146–3154 (2019)
Wang, L., Li, R., Zhang, C., Fang, S., Duan, C., Meng, X., Atkinson, P.M.: Unetformer: a unet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery. ISPRS J. Photogramm. Remote. Sens. 190, 196–214 (2022)
Yu, C., Gao, C., Wang, J., Yu, G., Shen, C., Sang, N.: Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation. Int. J. Comput. Vis. 129, 3051–3068 (2021)
Acknowledgment
The work was supported by the National Natural Science Foundation of China under Grant 42201415; the Hubei Natural Science Foundation of China under Grant 2022CFB607.
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Zheng, X. et al. (2024). MSCFNet: A Multi-scale Spatial and Channel Fusion Network for Geological Environment Remote Sensing Interpreting. In: Zhang, W., Tung, A., Zheng, Z., Yang, Z., Wang, X., Guo, H. (eds) Web and Big Data. APWeb-WAIM 2024. Lecture Notes in Computer Science, vol 14963. Springer, Singapore. https://doi.org/10.1007/978-981-97-7238-4_2
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DOI: https://doi.org/10.1007/978-981-97-7238-4_2
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