Abstract
High resolution remote sensing images that can show more detailed ground information play an important role in land classification. However, existing segmentation methods have the problems of insufficient use of multi-scale feature and semantic information. In this study, a multi-scale and cascade semantic segmentation network (MCNet) was proposed and tested on the Potsdam and Vaihingen datasets. (1) Multi-scale feature extraction module: using dilated convolution and a parallel structure to fully extract multi-scale feature information. (2) Cross-layer feature selection module: adaptively selecting features in different levels to avoid the loss of key features. (3) Multi-scale object guidance module: weighting the features at different scales to express the multi-scale ground objects. (4) Cascade structure in the decoder part: increasing the information flow and enhancing the decoding capability of the network. Results show that the proposed MCNet outperformed the baseline networks, achieving an average overall accuracy of 86.91% and 87.82% on the two datasets, respectively. In conclusion, the multi-scale and cascade semantic segmentation network can improve the accuracy of land cover classification by using remote sensing images.
Y. Zhou and T. Li—Equal contribution.
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References
Wang, M., Dong, Z., Cheng, Y., et al.: Optimal segmentation of high-resolution remote sensing image by combining superpixels with the minimum spanning tree. IEEE Trans. Geosci. Remote Sens. 56(1), 228–238 (2017)
Chen, S., Sun, T., Yang, F., et al.: An improved optimum-path forest clustering algorithm for remote sensing image segmentation. Comput. Geosci. 112, 38–46 (2018)
Wang, M., Wan, Y., Ye, Z., et al.: Remote sensing image classification based on the optimal support vector machine and modified binary coded ant colony optimization algorithm. Inf. Sci. 402, 50–68 (2017)
Chen, G., Tan, X., Guo, B., et al.: SDFCNv2: An improved FCN framework for remote sensing images semantic segmentation. Remote Sens. 13(23), 4902 (2021)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. arXiv preprint arXiv:1605.06211 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. arXiv preprint arXiv:1505.04597 (2015)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Chen, L.C., Papandreou, G., Kokkinos, I., et al.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv preprint arXiv:1412.7062 (2014)
Chen, L.C., Papandreou, G., Kokkinos, I., et al.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
Chen, L.C., Papandreou, G., Schroff, F., et al.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Wang, X., Girshick, R., Gupta, A., et al.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)
Li, S., Xue, L., Feng, L., et al.: Object detection network pruning with multi-task information fusion. World Wide Web 25(4), 1667–1683 (2022)
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, pp. 770–778 (2016)
Zhou, Q., Yang, W., Gao, G., et al.: Multi-scale deep context convolutional neural networks for semantic segmentation. World Wide Web 22, 555–570 (2019)
Zhou, Z., Zhou, Y., Wang, D., et al.: Self-attention feature fusion network for semantic segmentation. Neurocomputing 453, 50–59 (2021)
Zhao, Q., Liu, J., Li, Y., et al.: Semantic segmentation with attention mechanism for remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1–13 (2021)
Li, F., Wang, X., Sun, Y., et al.: Transfer learning based cascaded deep learning network and mask recognition for COVID-19. World Wide Web, pp. 1–16 (2023)
Liu, R., Mi, L., Chen, Z.: AFNet: adaptive fusion network for remote sensing image semantic segmentation. IEEE Trans. Geosci. Remote Sens. 59(9), 7871–7886 (2020)
Chen, X., Li, Z., Jiang, J., et al.: Adaptive effective receptive field convolution for semantic segmentation of VHR remote sensing images. IEEE Trans. Geosci. Remote Sens. 59(4), 3532–3546 (2020)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
Wang, P., Chen, P., Yuan, Y., et al.: Understanding convolution for semantic segmentation. arXiv preprint arXiv:1702.08502 (2017)
Wang, Q., Wu, B., Zhu, P., et al.: ECA-net: efficient channel attention for deep convolutional neural networks. arXiv preprint arXiv:1910.03151 (2019)
Xiang, S., Xie, Q., Wang, M.: Semantic segmentation for remote sensing images based on adaptive feature selection network. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2021)
Acknowledgments
This work was supported by Natural Science Foundation of China (No. U21A2013 and 42071430), Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education (Grant Number: GLAB2020ZR14 and CUG2022ZR02) and College Students’ Independent Innovation Funding Program Launch Project (No. S202310491229 and S202310491175).
Computation of this work was performed by the High-performance GPU Server (TX321203) Computing Centre of the National Education Field Equipment Renewal and Renovation Loan Financial Subsidy Project of China University of Geosciences, Wuhan.
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Zhou, Y., Li, T., Li, X., Feng, R. (2024). MCNet: A Multi-scale and Cascade Network for Semantic Segmentation of Remote Sensing Images. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14332. Springer, Singapore. https://doi.org/10.1007/978-981-97-2390-4_12
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