5,645
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Fanet: A deep learning framework for black and odorous water extraction

ORCID Icon, ORCID Icon, , , &
Article: 2234077 | Received 15 Apr 2022, Accepted 04 Jul 2023, Published online: 14 Jul 2023

References

  • 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), 2481–16. https://doi.org/10.1109/TPAMI.2016.2644615
  • Cao, J., Sun, Q., Zhao, D., Xu, M., Shen, Q., Wang, D., Wang, Y., & Ding, S. (2020). A critical review of the appearance of black-odorous waterbodies in China and treatment methods. Journal of Hazardous Materials, 385, 121511. https://doi.org/10.1016/j.jhazmat.2019.121511
  • Cheng, J., Wu, E., Che, Y., & Xu, Q. (2006). Study on key indicators for judging black and odorous water in area of plain river system. China Water Wastewater, 22, 18–22. https://doi.org/10.3321/j.issn:1000-4602.2006.09.005
  • Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. 2015 3rd International Conference on Learning Representations (ICLR), San Diego, USA. https://doi.org/10.48550/arXiv.1412.7062
  • Chen, Z., Xu, Q., Cong, R., & Huang, Q. (2020). Global context-aware progressive aggregation network for salient object detection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(7), 10599–10606. https://doi.org/10.1609/aaai.v34i07.6633
  • Chen, S., Zhao, W., & Liao, Z. (2021). Remote sensing identification of black-odor water bodies: A review. Remote Sensing for Natural Resources, 33, 20–29. https://doi.org/10.6046/gtzyyg.2020104
  • Csurka, G., & Perronnin, F. (2011). An efficient approach to semantic segmentation. International Journal of Computer Vision, 95(2), 198–212. https://doi.org/10.1007/s11263-010-0344-8
  • Denton, E., Chintala, S., Szlam, A., & Fergus, R. (2015). Deep generative image models using a Laplacian pyramid of adversarial networks. Advances in Neural Information Processing Systems, 28, 1486–1494. https://doi.org/10.48550/arXiv.1506.05751
  • Duan, H., Ma, R., Loiselle, S. A., Shen, Q., Yin, H., & Zhang, Y. (2014). Optical characterization of black water blooms in eutrophic waters. Science of the Total Environment, 482, 174–183. https://doi.org/10.1016/j.scitotenv.2014.02.113
  • Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013). Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1915–1929. https://doi.org/10.1109/TPAMI.2012.231
  • Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672–2680. https://doi.org/10.5555/2969033.2969125
  • Grangier, D., Bottou, L., & Collobert, R. (2009). Deep convolutional networks for scene parsing. 2009 26th International Conference on International Conference on Machine Learning (ICML), Montreal, Canada: ACM.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. 2015International Conference on Computer Vision (ICCV), Santiago, Chile:IEEE
  • Huang, Z., & Zheng, J. (2019). Extraction of black and odorous water based on aerial hyperspectral CASI image. 2019 39th International Geoscience and Remote Sensing Symposium (IGARSS) Yokohama, Japan:IEEE
  • Hu, C., Hackett, K. E., Callahan, M. K., Andréfouët, S., Wheaton, J. L., Porter, J. W., & Muller Karger, F. E. (2003). The 2002 ocean color anomaly in the Florida Bight: A cause of local coral reef decline? Geophysical Research Letters, 30(3). https://doi.org/10.1029/2002GL016479
  • Hu, C., Muller Karger, F. E., Vargo, G. A., Neely, M. B., & Johns, E. (2004). Linkages between coastal runoff and the Florida keys ecosystem: A study of a dark plume event. Geophysical Research Letters, 31(15). https://doi.org/10.1029/2004GL020382
  • Hu, F., Xia, G., Hu, J., & Zhang, L. (2015). Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11), 14680–14707. https://doi.org/10.3390/rs71114680
  • Jiang, Y., Li, C., Song, H., & Wang, W. (2022). Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks. Journal of Hazardous Materials, 432, 128732. https://doi.org/10.1016/j.jhazmat.2022.128732
  • Jiang, Y., Li, C., Zhang, Y., Zhao, R., Yan, K., & Wang, W. (2021). Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (FOG) of sewer networks in urban commercial areas. Water Research, 207, 117797. https://doi.org/10.1016/j.watres.2021.117797
  • Jiang, Y., Zhou, N., Zhou, Y., Huang, R., & Zhong, Z. (2019). Research on remote sensing monitoring of urban black and odorous water. Bulletin of Surveying & Mapping, 98–104. https://doi.org/10.13474/j.cnki.11-2246.2019.0522
  • Kohli, P., Ladicky, L., & Torr, P. H. S. (2009). Robust higher order potentials for enforcing label consistency. International Journal of Computer Vision, 82(3), 302–324. https://doi.org/10.1007/s11263-008-0202-0
  • Koller, D., & Friedman, N. (2009). Undirected graphical models. In D. Koller (Ed.), Probabilistic graphical models: Principles and techniques (pp. 101–105). CRC Press.
  • Li, Z., Duan, H., Shen, Q., Zhang, Y., & Ma, R. (2015). The changes of water color induced by chromophoric dissolved organic matter (CDOM) during the formation of black blooms. Journal of Lake Science, 27(4), 616–622. https://doi.org/10.18307/2015.0408
  • Li, X., Niu, Z., Jiang, S., & Jin, Y. (2012). Satellite remote sensing monitoring of black color water blooms in lake Taihu. The Administration and Technique of Environmental Monitoring, 24(2), 12–17. https://doi.org/10.3969/j.issn.1006-2009.2012.02.003
  • Lin, G., Shen, C., Van Den Hengel, A., & Reid, I. (2016). Efficient piecewise training of deep structured models for semantic segmentation. 2016 30th Computer Vision and Pattern Recognition (CVPR), Nevada, USA, IEEE.
  • Liu, D., Li, S., & Cao, Z. (2016). State-of-the-art on deep learning and its application in image object classification and detection. Computer Science, 43(12), 13–23. https://doi.org/10.11896/j.issn.1002-137X.2016.12.003
  • Long, J., Shelhamer, E., & Darrell, T. 2015 Fully convolutional networks for semantic segmentation 2015 29th Computer Vision and Pattern Recognition (CVPR) Boston, IEEE
  • Luc, P., Couprie, C., Chintala, S., & Verbeek, J. (2016). Semantic segmentation using adversarial networks. Advances in Neural Information Processing Systems, 1–12. https://doi.org/10.48550/arXiv.1611.08408
  • Ma, X., Wang, L., Qi, K., & Zheng, G. (2021). Remote sensing image scene classification method based on multi-scale cyclic attention network. Earth Science, 46(10), 3740–3752. https://doi.org/10.3799/dqkx.2020.365
  • Mosinska, A., Marquez-Neila, P., Koziński, M., & Fua, P. (2018). Beyond the pixel-wise loss for topology-aware delineation. 2018 32th Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah: IEEE.
  • Nichol, J. E. (1993a). Remote sensing of tropical blackwater rivers: A method for environmental water quality analysis. Applied Geography, 13(2), 153–168. https://doi.org/10.1016/0143-6228(93)90056-7
  • Nichol, J. E. (1993b). Remote-sensing of water-quality in the Singapore-Johor-Riau growth triangle. Remote Sensing of Environment, 43(2), 139–148. https://doi.org/10.1016/0034-4257(93)90003-G
  • Prahs, P., Radeck, V., Mayer, C., Cvetkov, Y., Cvetkova, N., Helbig, H., & Märker, D. (2018). OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications. Graefe’s Archive for Clinical and Experimental Ophthalmology, 256(1), 91–98. https://doi.org/10.1007/s00417-017-3839-y
  • Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. Advances in Neural Information Processing Systems. https://doi.org/10.48550/arXiv.1511.06434
  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. 2015 18th International Conference on Medical image computing and computer-assisted intervention (MICCAI), Munich, Germany: Springer.
  • Serte, S., & Demirel, H. (2021). Deep learning for diagnosis of COVID-19 using 3D CT scans. Computers in Biology and Medicine, 132, 104306. https://doi.org/10.1016/j.compbiomed.2021.104306
  • Shao, H., Ding, F., Yang, J., & Zheng, Z. (2021). Remote sensing information extraction of black and odorous water based on deep learning. Journal of Yangtze River Science Research Institute, 1–10. https://doi.org/10.11988/ckyyb.20210045
  • Shouno, H., Suzuki, S., & Kido, S. (2015). A transfer learning method with deep convolutional neural network for diffuse lung disease classification. 2015 22nd International Conference on Neural Information Processing (ICONIP), Istanbul, Turkey: Springer.
  • Simonyan, K., & Zisserman, A. (2015) Very deep convolutional networks for large-scale image recognition. 2015 4th International Conference of Legal Regulators(ICLR)Toronto, Canada: OpenReview.net.
  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958. https://www.jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf?utm_content=buffer79b43&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
  • Summers, J. (1986). Water quality modeling of the Huangpu River in Shanghai, China. 1986 National Environmental Engineering Conference: Use and Abuse of Environmental Information in Engineering, Melbourne, Victoria: InformIT.
  • Tan, L., Lv, X., Lian, X., & Wang, G. (2021). Yolov4_drone: UAV image target detection based on an improved YOLOv4 algorithm. Computers & Electrical Engineering, 93, 107261. https://doi.org/10.1016/j.compeleceng.2021.107261
  • Tian, C., Xu, Y., Li, Z., Zuo, W., Fei, L., & Liu, H. (2020). Attention-guided CNN for image denoising. Neural Networks, 124, 117–129. https://doi.org/10.1016/j.neunet.2019.12.024
  • Tung, T. M., Yaseen, Z. M., & Yaseen, Z. M. (2020). A survey on river water quality modelling using artificial intelligence models: 2000–2020. Journal of Hydrology, 585, 124670. https://doi.org/10.1016/j.jhydrol.2020.124670
  • Tung, T. M., Yaseen, Z. M., & Yaseen, Z. M. (2021). Deep learning for prediction of water quality index classification: Tropical catchment environmental assessment. Natural Resources Research, 30(6), 4235–4254. https://doi.org/10.1007/s11053-021-09922-5
  • Wang, Q., Xu, J., Chen, Y., Li, J., & Wang, X. (2012). Influence of the varied spatial resolution of remote sensing images on Urban and rural residential information extraction. Resources Science, 34(1), 159–165. https://do.org/CNKI:SUN:ZRZY.0.2012-01-024
  • Wen, S., Wang, Q., Li, Y., Zhu, L., Lü, H., Lei, S., Ding, X., & Miao, S. (2018). Remote sensing identification of urban black-odor water bodies based on high-resolution images: A case study in Nanjing. Environmental Sciences, 39(1), 57–67. https://doi.org/10.13227/j.hjkx.201703264
  • Wu, S. (2019). Research progress of remote sensing monitoring key technologies for urban black and odorous water bodies. Chinese Journal of Environmental Engineering, 13(6), 1261–1271. https://doi.org/10.12030/j.cjee.201812020
  • Wu, Y., Han, P., & Zheng, Z. (2021). Instant water body variation detection via analysis on remote sensing imagery. Journal of Real-Time Image Processing, 18(5), 1577–1590. https://doi.org/10.1007/s11554-020-01062-y
  • Yao, H., Lu, Y., & Gong, Z. (2019). Remote sensing identification of urban black and odorous water body based on PlanetScope images: A case study in Qinzhou, Guangxi. Environmental Engineering, 37(10), 35–43. https://do.org/CNKI:SUN:YGXB.0.2019-02-005
  • Yao, Y., Shen, Q., Zhu, L., Gao, H., Cao, H., Han, H., Sun, J., & Li, J. (2019). Remote sensing identification of urban black-odor water bodies in Shenyang city based on GF-2 image. Journal of Remote Sensing, 23(2), 230–242. https://doi.org/10.11834/jrs.20197482
  • Yaseen, Z. M. (2021). An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. Chemosphere, 277, 130126. https://doi.org/10.1016/j.chemosphere.2021.130126
  • Yuan, P., Xu, L., Baoling, K. E., Sun, F., & Gao, H. (2020). Treatment and ecological restoration of black and odorous water body in Yueya Lake in Nanjing City. Journal of Environmental Engineering Technology, 10(5), 696–701. https://doi.org/10.12153/j.issn.1674-991X.20200111
  • Zhang, Y., Shang, J., & Yu, X. (2005). Application of principal component-cluster analysis complex model to water environment management: A case study in Songhua River in Jilin section as an example. Advances in Water Science, 16(4), 592. https://doi.org/10.14042/j.cnki.32.1309.2005.04.021
  • Zhao, J., Hu, C., Lapointe, B., Melo, N., Johns, E. M., & Smith, R. H. (2013). Satellite-observed black water events off Southwest Florida: Implications for coral reef health in the florida keys national marine sanctuary. Remote Sensing, 5(1), 415–431. https://doi.org/10.3390/rs5010415
  • Zhao, Y., Zheng, G., Xu, Z., Qiu, Z., & Chen, Z. (2022). Multiscale feature weighted-aggregating and boundary enhancement network for semantic segmentation of high-resolution remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 8118–8130. https://doi.org/10.1109/JSTARS.2022.3205609
  • Zheng, G., Le, X., Wang, H., & Hua, W. (2017). Inversion of water depth from WorldView-02 satellite imagery based on BP and RBP neural network. Earth Science, 42(12), 2345–2353. https://doi.org/10.3799/dqkx.2017.552
  • Zheng, G., Pan, Z., Meng, Y., & Wang, H. (2021). Inversion of sea surface flow field in Southern South China sea based on satellite remote sensing data. Earth Science, 46(1), 341–349. https://doi.org/10.3799/dqkx.2020.250