{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T01:30:41Z","timestamp":1778895041474,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":38,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819787425","type":"print"},{"value":"9789819787432","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-8743-2_5","type":"book-chapter","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:57:22Z","timestamp":1730303842000},"page":"55-72","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["GAN-Based Defogging and Multiscale Fusion Approach for UAV-Based Seagrass Bed Imagery Semantic Segmentation in Challenging Marine Environments"],"prefix":"10.1007","author":[{"given":"Liang","family":"Qu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoli","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengmeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruobing","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengke","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"issue":"2","key":"5_CR1","doi-asserted-by":"publisher","first-page":"42","DOI":"10.3390\/drones6020042","volume":"6","author":"B Bollard","year":"2022","unstructured":"Bollard, B., Doshi, A., Gilbert, N., et al.: Drone technology for monitoring protected areas in remote and fragile environments. Drones 6(2), 42 (2022)","journal-title":"Drones"},{"issue":"2","key":"5_CR2","doi-asserted-by":"publisher","first-page":"140","DOI":"10.3390\/drones7020140","volume":"7","author":"S Kim","year":"2023","unstructured":"Kim, S., Lee, C.W., Park, H.J., et al.: Piloting an unmanned aerial vehicle to explore the floristic variations of inaccessible cliffs along Island coasts. Drones 7(2), 140 (2023)","journal-title":"Drones"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wang, C., Liu, R., et al.: Self-augmented unpaired image dehazing via density and depth decomposition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2037\u20132046 (2022)","DOI":"10.1109\/CVPR52688.2022.00208"},{"key":"5_CR4","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.14017","volume":"10","author":"S Tahara","year":"2022","unstructured":"Tahara, S., Sudo, K., Yamakita, T., et al.: Species level mapping of a seagrass bed using an unmanned aerial vehicle and deep learning technique. PeerJ 10, e14017 (2022)","journal-title":"PeerJ"},{"issue":"11","key":"5_CR5","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"60","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial networks. Commun. ACM 60(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Dong, H., Pan, J., Xiang, L., et al.: Multiscale boosted dehazing network with dense feature fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2157\u20132167 (2020)","DOI":"10.1109\/CVPR42600.2020.00223"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., et al.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"5_CR8","doi-asserted-by":"publisher","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","volume":"129","author":"C Yu","year":"2021","unstructured":"Yu, C., Gao, C., Wang, J., et al.: Bisenet v2: bilateral network with guided aggrega - tion for real-timesemantic segmentation. Int. J. Comput. Vision 129, 3051\u20133068 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"5_CR9","unstructured":"Wang, J., Gou, C., Wu, Q., et al.: Rtformer: efficient design for real-time semantic segmentation with transformer. arXiv preprint arXiv:2210.07124 (2022)"},{"key":"5_CR10","unstructured":"Guo, M.H., Lu, C.Z., Hou, Q., et al.: Segnext: rethinking convolutional attention design for semantic segmentation. arXiv preprint arXiv: 2209.08575 (2022)"},{"key":"5_CR11","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"key":"5_CR12","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"5_CR13","unstructured":"Chen, X., Duan, Y., Houthooft, R., et al.: Infogan: interpretable representation learning by information maximizing generative adversarial nets. Adv. Neural Inform. Process. Syst. 29 (2016)"},{"key":"5_CR14","unstructured":"Su, J.: O-GAN: extremely concise approach for autoencoding generative adversarial networks. arXiv preprint arXiv:1903.01931 (2019)"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., et al.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Wang, T., Liu, M., Zhu, J.: pix2pixhd: highresolution image synthesis and semantic manipulation with conditional GANs. In: IEEE CVF Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00917"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., et al.: Photo-realistic single image superresolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., et al.: StarGAN: unified generative adversarial networks for multidomain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8789\u20138797 (2018)","DOI":"10.1109\/CVPR.2018.00916"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder - decoder architecture for image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. [18], 39(12), 2481\u20132495 (2017)","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Niu, Z., Liu, W., Zhao, J., et al.: Deeplab-based spatial feature extraction for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. [19], 16(2), 251\u2013255 (2018)","DOI":"10.1109\/LGRS.2018.2871507"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"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. Patt. Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","DOI":"10.1109\/TPAMI.2017.2699184"},{"issue":"2","key":"5_CR25","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1504\/IJMIC.2020.116199","volume":"36","author":"H Si","year":"2020","unstructured":"Si, H., Shi, Z., Hu, X., et al.: Image semantic segmentation based on improved deeplab v3 model. Int. J. Model. Identific. Control 36(2), 116\u2013125 (2020)","journal-title":"Int. J. Model. Identific. Control"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Si, Y., Gong, D., Guo, Y., et al.: An advanced spectral\u2013spatial classification framework for hyperspectral imagery based on deeplab v3+. Appl. Sci. [22], 11(12), 5703 (2021)","DOI":"10.3390\/app11125703"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., et al.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, [23], pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., et al.: Learning a discriminative feature network for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1857\u20131866 (2018)","DOI":"10.1109\/CVPR.2018.00199"},{"key":"5_CR29","unstructured":"Paszke, A., Chaurasia, A., Kim, S., et al.: Enet: a deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147 (2016)"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., Culurciello, E.: Linknet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP). IEEE [26], pp. 1\u20134 (2017)","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., et al.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 325\u2013341 (2018)","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Li, H., Xiong, P., Fan, H., et al.: Dfanet: deep feature aggregation for realtime semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9522\u20139531 (2019)","DOI":"10.1109\/CVPR.2019.00975"},{"key":"5_CR33","doi-asserted-by":"publisher","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","volume":"129","author":"C Yu","year":"2021","unstructured":"Yu, C., Gao, C., Wang, J., et al.: Bisenet v2: bilateral network with guided aggregation for real-time semantic segmentation. Int. J. Comput. Vision 129, 3051\u20133068 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Fan, M., Lai, S., Huang, J., et al.: Rethinking bisenet for real-time semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9716\u20139725 (2021)","DOI":"10.1109\/CVPR46437.2021.00959"},{"key":"5_CR35","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., et al.: Enhanced deep residual networks for single image superresolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"issue":"11","key":"5_CR36","doi-asserted-by":"publisher","first-page":"5187","DOI":"10.1109\/TIP.2016.2598681","volume":"25","author":"B Cai","year":"2016","unstructured":"Cai, B., Xu, X., Jia, K., et al.: Dehazenet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187\u20135198 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"5_CR37","doi-asserted-by":"crossref","unstructured":"Li, B., Peng, X., Wang, Z., et al.: Aod-net: all-in-one dehazing network. In: Proceedings of the IEEE International Conference on Computer Vision, [33], pp. 4770\u20134778 (2017)","DOI":"10.1109\/ICCV.2017.511"},{"key":"5_CR38","doi-asserted-by":"crossref","unstructured":"Qin, X., Wang, Z., Bai, Y., et al.: Ffa-net: feature fusion attention network for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34. pp. 11908\u201311915 (2020)","DOI":"10.1609\/aaai.v34i07.6865"}],"container-title":["Communications in Computer and Information Science","Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8743-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T16:05:18Z","timestamp":1730304318000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8743-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819787425","9789819787432"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8743-2_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"ICPCSEE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of Pioneering Computer Scientists, Engineers and Educators","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpcsee2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.icpcsee.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}