FMPNet: a fuzzy-embedded multi-scale prototype network for sea-land segmentation of remote sensing images

References

• Aghdami-Nia, M., Shah-Hosseini, R., Rostami, A., & Homayouni, S. (2022). Automatic coastline extraction through enhanced sea-land segmentation by modifying Standard U-Net. International Journal of Applied Earth Observation Geoinformation, 109, 102785. https://doi.org/10.1016/j.jag.2022.102785
   Web of Science ®Google Scholar
• Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, 1–15. https://doi.org/10.1186/s40537-021-00444-8
   PubMedGoogle Scholar
• Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39(12), 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615
   PubMed Web of Science ®Google Scholar
• Barceló, M., Vargas, C. A., & Gelcich, S. (2023). Land–sea interactions and ecosystem services: research gaps and future challenges. Sustainability Gelcich, 15(10), 8068. https://doi.org/10.3390/su15108068
   Web of Science ®Google Scholar
• Chaurasia, A., & Culurciello, E. (2017). Linknet: Exploiting encoder representations for efficient semantic segmentation. IEEE Visual Communications and Image Processing (VCIP). https://doi.org/10.1109/VCIP.2017.8305148
   Google Scholar
• Cheng, D., Meng, G., Cheng, G., & Pan, C. (2016). SeNet: Structured edge network for sea–land segmentation. IEEE Geoscience & Remote Sensing Letters, 14(2), 247–251. https://doi.org/10.1109/LGRS.2016.2637439
   Web of Science ®Google Scholar
• Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis & Machine Intelligence, 40(4), 834–848. https://doi.org/10.1109/TPAMI.2017.2699184
   PubMed Web of Science ®Google Scholar
• Chen, X., Sun, J., Yin, K., & Yu, J. (2014). Sea-land segmentation algorithm of SAR image based on Otsu method and statistical characteristic of sea area. Journal of Data Acquisition and Processing, 29, 603–608.
   Google Scholar
• Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.
   Google Scholar
• Chong, Q., Xu, J., Jia, F., Liu, Z., Yan, W., Wang, X., & Song, Y. (2022). A multiscale fuzzy dual-domain attention network for urban remote sensing image segmentation. International Journal of Remote Sensing, 43(14), 5480–5501. https://doi.org/10.1080/01431161.2022.2135413
   Web of Science ®Google Scholar
• Chu, Z., Tian, T., Feng, R., & Wang, L. (2019). Sea-land segmentation with Res-UNet and fully connected CRF. IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/IGARSS.2019.8900625
   Google Scholar
• Cui, B., Jing, W., Huang, L., Li, Z., & Lu, Y. (2020). Sanet: A sea–land segmentation network via adaptive multiscale feature learning. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 14, 116–126. https://doi.org/10.1109/JSTARS.2020.3040176
   Web of Science ®Google Scholar
• Fang, L., Jiang, Y., Yan, Y., Yue, J., & Deng, Y. (2023). Hyperspectral image instance segmentation using spectral–spatial feature pyramid network. IEEE Transactions on Geoscience & Remote Sensing, 61, 1–13. https://doi.org/10.1109/TGRS.2023.3240481
   Web of Science ®Google Scholar
• Fonner, R., Bellanger, M., & Warlick, A. (2020). Economic analysis for marine protected resources management: Challenges, tools, and opportunities. Ocean & Coastal Management, 194, 105222. https://doi.org/10.1016/j.ocecoaman.2020.105222
   Web of Science ®Google Scholar
• Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P., & Garcia-Rodriguez, J. (2018). A survey on deep learning techniques for image and video semantic segmentation. Applied Soft Computing, 70, 41–65. https://doi.org/10.1016/j.asoc.2018.05.018.
   Web of Science ®Google Scholar
• Hao, S., Zhou, Y., & Guo, Y. (2020). A brief survey on semantic segmentation with deep learning. Neurocomputing, 406, 302–321. https://doi.org/10.1016/j.neucom.2019.11.118
   Web of Science ®Google Scholar
• Heidler, K., Mou, L., Baumhoer, C., Dietz, A., & Xiang Zhu, X. (2021). HED-UNet: Combined segmentation and edge detection for monitoring the Antarctic coastline. IEEE Transactions on Geoscience & Remote Sensing, 60, 1–14. https://doi.org/10.1109/tgrs.2021.3064606
   Web of Science ®Google Scholar
• He, H., Xing, H., Hu, D., & Yu, X. (2019). Novel fuzzy uncertainty modeling for land cover classification based on clustering analysis. Science China Earth Sciences, 62, 438–450. https://doi.org/10.1007/s11430-017-9224-6
   Web of Science ®Google Scholar
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.
   Google Scholar
• Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.
   Google Scholar
• Hu, K., Zhang, E., Xia, M., Weng, L., & Lin, H. (2023). Mcanet: A multi-branch network for cloud/snow segmentation in high-resolution remote sensing images. Remote Sensing, 15(4), 1055.
   Web of Science ®Google Scholar
• Ji, X., Tang, L., Lu, T., & Cai, C. (2023). Dbenet: Dual-branch ensemble network for sea-land segmentation of remote sensing images. IEEE transactions on instrumentation and measurement, 72, 1–11. https://doi.org/10.1109/tim.2023.3302376
   PubMed Web of Science ®Google Scholar
• Ji, Y., Zhang, H., Zhang, Z., & Liu, M. (2021). CNN-based encoder-decoder networks for salient object detection: A comprehensive review and recent advances. Information Sciences, 546 , 835–857. https://doi.org/10.1016/j.ins.2020.09.003
   Web of Science ®Google Scholar
• Kotaridis, I., & Lazaridou, M. (2021). Remote sensing image segmentation advances: A meta-analysis. ISPRS Journal of Photogrammetry Lazaridou, and Remote Sensing, 173 , 309–322. https://doi.org/10.1016/j.isprsjprs.2021.01.020
   Web of Science ®Google Scholar
• Lei, T., Xiaohong, J., Yanning, Z., Shigang, L., Hongying, M., & Asoke, K. (2018). Superpixel-based fast fuzzy C-means clustering for color image segmentation. J IEEE Transactions on Fuzzy Systems Nandi, 27(9), 1753–1766.
   Web of Science ®Google Scholar
• Li, X., He, Z., Jiang, L., & Ye, Y. (2018). Research on Remote Sensing Dynamic Monitoring of Ecological Resource Environment Based on GIS. Wireless Personal Communications, 102(4), 2941–2953. https://doi.org/10.1007/s11277-018-5317-1
   Web of Science ®Google Scholar
• Li, J., Huang, Z., Wang, Y., & Luo, Q. (2022). Sea and land segmentation of optical remote sensing images based on U-Net optimization. Remote Sensing, 14(17), 4163.
   Web of Science ®Google Scholar
• Li, R., Liu, W., Yang, L., Sun, S., Hu, W., Zhang, F., & Li, W. (2018). DeepUNet: A deep fully convolutional network for pixel-level sea-land segmentation. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 11(11), 3954–3962.
   Web of Science ®Google Scholar
• Lin, Q., Chen, X., Chen, C., & Garibaldi, J. M. (2022). A novel quality control algorithm for medical image segmentation based on Fuzzy Uncertainty. IEEE Transactions on Fuzzy Systems, 31, 2532–2544. https://doi.org/10.1109/tfuzz.2022.3228332
   Web of Science ®Google Scholar
• Lin, H., Shi, Z., & Zou, Z. (2017). Maritime semantic labeling of optical remote sensing images with multi-scale fully convolutional network. Remote Sensing, 9(5), 480.
   Web of Science ®Google Scholar
• Liu, X., Deng, Z., & Yang, Y. (2019). Recent progress in semantic image segmentation. Artificial Intelligence Review, 52, 1089–1106. https://doi.org/10.1007/s10462-018-9641-3
   Web of Science ®Google Scholar
• Liu, X., Tian, X., Lin, S., Qu, Y., Ma, L., Yuan, W., Zhang, Z., & Xie, Y. (2021). Learn from concepts: towards the purified memory for few-shot learning. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal-themed Virtual Reality.
   Google Scholar
• Liu, Y., Wu, Y.-H., Wen, P., Shi, Y., Qiu, Y., & Cheng, M.-M. (2020). Leveraging instance-, image-and dataset-level information for weakly supervised instance segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 44(3), 1415–1428.
   Web of Science ®Google Scholar
• Li, X., Wang, W., Hu, X., & Yang, J. (2019). Selective kernel networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.
   Google Scholar
• Li, X., Xu, F., Liu, F., Lyu, X., Tong, Y., Xu, Z., & Zhou, J. (2023). A Synergistical Attention Model for Semantic Segmentation of Remote Sensing Images. IEEE Transactions on Geoscience & Remote Sensing, 61, 1–16. https://doi.org/10.1109/tgrs.2023.3243954
   Web of Science ®Google Scholar
• Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.
   Google Scholar
• Ma, X., Xu, J., Chong, Q., Ou, S., Xing, H., & Ni, M. (2023). Fcunet: Refined remote sensing image segmentation method based on a fuzzy deep learning conditional random field network. IET Image Processing, 17, 3616–3629. https://doi.org/10.1049/ipr2.12870
   Web of Science ®Google Scholar
• Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., & Terzopoulos, D. (2021). Image segmentation using deep learning: A survey. IEEE Transactions on Pattern Analysis & Machine Intelligence, 44(7), 3523–3542.
   Web of Science ®Google Scholar
• Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., & Kainz, B. (2018). Attention u-net: Learning where to look for the pancreas. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.
   Google Scholar
• Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.
   Web of Science ®Google Scholar
• Qu, T., Xu, J., Chong, Q., Liu, Z., Yan, W., Wang, X., Song, Y., & Ni, M. (2023). Fuzzy neighbourhood neural network for high-resolution remote sensing image segmentation. European Journal of Remote Sensing, 56(1), 2174706.
   Web of Science ®Google Scholar
• Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.
   Google Scholar
• Shamsolmoali, P., Zareapoor, M., Wang, R., Zhou, H., & Yang, J. (2019). A novel deep structure U-Net for sea-land segmentation in remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 12(9), 3219–3232.
   Web of Science ®Google Scholar
• Singh, V., Dev, R., Dhar, N. K., Agrawal, P., & Verma, N. K. (2018). Adaptive type-2 fuzzy approach for filtering salt and pepper noise in grayscale images. IEEE Transactions on Fuzzy Systems, 26(5), 3170–3176.
   Web of Science ®Google Scholar
• Sun, W., Chen, C., Liu, W., Yang, G., Meng, X., Wang, L., & Ren, K. (2023). Coastline extraction using remote sensing: A review. GIScience & Remote Sensing, 60(1), 2243671.
   Web of Science ®Google Scholar
• Tian, Z., Zhang, B., Chen, H., & Shen, C. (2022). Instance and panoptic segmentation using conditional convolutions. IEEE Transactions on Pattern Analysis & Machine Intelligence, 45(1), 669–680.
   PubMed Web of Science ®Google Scholar
• Tseng, S.-H., & Sun, W.-H. (2022). Sea–Land Segmentation Using HED-UNET for Monitoring Kaohsiung Port. Mathematics, 10(22), 4202.
   Web of Science ®Google Scholar
• Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., & Wang, X. (2020). Deep high-resolution representation learning for visual recognition. IEEE Transactions on Pattern Analysis & Machine Intelligence, 43(10), 3349–3364.
   Web of Science ®Google Scholar
• Wu, X., Sahoo, D., & Hoi, S. C. (2020). Recent advances in deep learning for object detection. Neurocomputing, 396, 39–64. https://doi.org/10.1016/j.neucom.2020.01.085
   Web of Science ®Google Scholar
• Zhao, T., Xu, J., Chen, R., & Ma, X. (2021). Remote sensing image segmentation based on the fuzzy deep convolutional neural network. International Journal of Remote Sensing, 42(16), 6264–6283.
   Web of Science ®Google Scholar