Semantic segmentation of algal blooms on the ocean surface using Sentinel 3 CHL_NN band imagery

• Autorzy: Bhandage Venkatesh , Pai M. M Manohara

Identyfikatory

DOI

10.35784/acs-2024-27

• Języki publikacji: EN

Abstrakty

EN

Satellite imagery plays an important role in detecting algal blooms because of its ability to cover larger geographical regions. Excess growth of Sea surface algae, characterized by the presence of Chlorophyll-a (Chl-a), is considered to be harmful. The detection of algal growth at an earlier stage may prevent hazardous effects on the aquatic environment. Semantic segmentation of algal blooms is helpful in the quantization of algal blooms. A rule-based semantic segmentation approach for the segregation of sea surface algal blooms is proposed. Bloom concentrations are classified into three different concentrations, namely, low, medium, and high. The chl_nn band in the Sentinel-3 satellite images is used for experimentation. The chl_nn band has exclusive details of the presence of chlorophyll concentrations. A dataset is proposed for the semantic segmentation of algal blooms. The devised rule-based semantic segmentation approach has produced an average accuracy of 98%. A set of 100 images is randomly selected for testing. The tests are repeated on 5 different image sets. The results are validated by the pixel comparison method. The proposed work is compared with other relevant works. The Arabian Sea near the coastal districts of Udupi and Mangaluru has been considered as the area of study. The methodology can be adapted to monitor the life cycle of blooms and their hazardous effects on aquatic life.

Słowa kluczowe

EN

Sentinel 3; algal bloom; remote sensing; chlorophyll-a; semantic segmentation; rule-based classifier

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2024
• Tom: Vol. 20, no. 3
• Strony: 34--50
• Opis fizyczny: Bibliogr. 41 poz., fig., tab.

Twórcy

autor

Bhandage Venkatesh

• Manipal Academy of Higher Education, Manipal Institute of Technology Department of Computer Science and Engineering, India

• https://orcid.org/0000-0002-9503-8196

autor

Pai M. M Manohara

• Manipal Academy of Higher Education, Manipal Institute of Technology, Department of Information and Communication Technology, India

• https://orcid.org/0000-0003-2164-2945

Bibliografia

• [1] Al-Nawashi, M. M., Al-Hazaimeh, O. M., & Khazaaleh, M. Kh. (2024). New approach for breast cancer detection based on machine learning techniques. Applied Computer Science, 20(1), 1-16. https://doi.org/10.35784/acs-2024-01
• [2] Anilkumar, P., & Venugopal, P. (2022). Research contribution and comprehensive review towards the semantic segmentation of aerial images using Deep Learning techniques. Security and Communication Networks, 2022(1), 6010912. https://doi.org/10.1155/2022/6010912
• [3] Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481-2495. https://doi.org/10.1109/TPAMI.2016.2644615
• [4] Baran, K. (2024). Application of thermal imaging cameras for smartphone: Seek Thermal Compact Pro and FLIR One Pro for human stress detection – Comparison and study. Applied Computer Science, 20(1), 122–138. https://doi.org/10.35784/acs-2024-08
• [5] Cui, B., Zhang, H., Jing, W., Liu, H., and Cui, J. (2022). SRSe-Net: Super-Resolution-Based semantic segmentation network for green tide extraction. Remote Sensing. 14(3), 710. https://doi.org/10.3390/rs14030710
• [6] Elbahri, M., Taleb, N., Ardjoun, S. A. E. M., & Zouaoui, C. M. A. (2024). Few-shot learning with pre-trained layers integration applied to hand gesture recognition for disabled people. Applied Computer Science, 20(2), 1-23. https://doi.org/10.35784/acs-2024-13
• [7] EUMETSAT. (2024). OLCI Level-2 Water Full Resolution. http://coda.eumetsat.int/#/home
• [8] Fernández-Tejedor, M., Velasco, J. E., & Angelats, E. (2022). Accurate estimation of chlorophyll-a concentration in the coastal areas of the ebro delta (NW Mediterranean) using Sentinel-2 and its application in the selection of areas for mussel aquaculture. Remote Sensing, 14(20), 5235. https://doi.org/10.3390/rs14205235
• [9] Fogg, G. E. (2022). Harmful algae - A perspective. Harmful Algae, 1(1), 1-4. https://doi.org/10.1016/S1568-9883(02)00002-1
• [10] Girisha, S., Pai, M. M. M., Verma, U., & Pai, R. M. (2021a). Semantic segmentation with enhanced temporal smoothness using CRF in aerial videos. IEEE Madras Section Conference (MASCON) (pp. 1-5). IEEE. https://doi.org/10.1109/MASCON51689.2021.9563599
• [11] Girisha, S., Verma, U., Manohara Pai, M. M., & Pai., R. M. (2021b). UVid-Net: Enhanced semantic segmentation of UAV aerial videos by embedding temporal information. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 4115-4127. https://doi.org/10.1109/JSTARS.2021.3069909
• [12] Haji Gholizadeh, M., Melesse, A. M., & Reddi, L. (2016). Spaceborne and airborne sensors in water quality assessment. International Journal of Remote Sensing, 37(14), 3143-3180. https://doi.org/10.1080/01431161.2016.1190477
• [13] Ho, J. C., Michalak, A. M., & Pahlevan, N. (2019). Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature, 574, 667-670. https://doi.org/10.1038/s41586-019-1648-7
• [14] Jaiganesh, S. N. N., Sarangi, R. K., & Shukla, S. (2021). Satellite-based observation of ocean productivity in southeast Arabian Sea using chlorophyll, sea surface temperature and wind datasets. Journal of Earth System Science, 130, 5. https://doi.org/10.1007/s12040-020-01512-y
• [15] Kamath, R., Balachandra, M., Vardhan, A., & Maheshwari, U. (2022). Classification of paddy crop and weeds using semantic segmentation. Cogent Engineering, 9(1), 2018791. https://doi.org/10.1080/23311916.2021.2018791
• [16] Kinane Daouadji, A., & Bendella, F. (2024). Improving e-learning by facial expression analysis. Applied Computer Science, 20(2), 126-137. https://doi.org/10.35784/acs-2024-20
• [17] Kotaridis, I., & Lazaridou, M. (2022). Semantic segmentation using a UNet architecture on Sentinel-2 data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2022, 119-126. https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-119-2022
• [18] Kutser, T. (2009). Passive optical remote sensing of cyanobacteria and other Intense phytoplankton blooms in coastal and inland waters. International Journal of Remote Sensing, 30(17), 4401-4425. https://doi.org/10.1080/01431160802562305
• [19] Li, Z., & Demir, I. (2023). U-net-based semantic classification for flood extent extraction using SAR imagery and GEE platform: A case study for 2019 central US flooding. Science of the Total Environment, 869, 161757. https://doi.org/10.1016/j.scitotenv.2023.161757
• [20] Lilay, M. Y., & Taye, G. D. (2023). Semantic segmentation model for land cover classification from satellite images in Gambella National Park, Ethiopia. SN Applied Sciences, 5, 76. https://doi.org/10.1007/s42452-023-05280-4
• [21] Ma, J., Zhou, W., Lei, J., & Yu, L. (2023). Adjacent bi-hierarchical network for scene parsing of remote sensing images. IEEE Geoscience and Remote Sensing Letters, 20, 3000705. https://doi.org/10.1109/LGRS.2023.3241648
• [22] Maiyanti, S. I., Desiani, A., Lamin, S., Puspitashati., Arhami, M., Gofar, N., & Cahyana, D. (2023) Rotation-gamma correction augmentation on CNN-dense block for soil image classification. Applied Computer Science, 19(3), 96-115. https://doi.org/10.35784/acs-2023-27
• [23] Makhlouf, Z., Meraoumia, A., Lakhdar, L., & Haouam, M. Y. (2024). Enhancing medical data security in e-health systems using biometric-based watermarking. Applied Computer Science, 20(1), 28-55. https://doi.org/10.35784/acs-2024-03
• [24] Nallapareddy, A., (2022). Detection and classification of vegetation areas from red and near infrared bands of Landsat-8 optical satellite image. Applied Computer Science, 18(1), 45-55. https://doi.org/10.35784/acs-2022-4
• [25] Nayak, R. K., Swapna, M., Manche, S. S., Mohanty, P. C., Sheshasai, M. V. R., Dadhwal, V. K., & Kumar, R. (2023). Assessment of chlorophyll-a seasonal cycle in the North Indian Ocean using observations from OCM2, MODIS, and SeaWiFS. Journal of the Indian Society of Remote Sensing, 51, 229-246. https://doi.org/10.1007/s12524-022-01642-4
• [26] Ogashawara, I. (2019). The use of Sentinel-3 imagery to monitor cyanobacterial blooms. Environments, 6(6), 60. https://doi.org/10.3390/environments6060060
• [27] Randolph, K., Wilson, J., Tedesco, L., Li, L., Pascual, D. L., & Soyeux, E. (2008) Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin. Remote Sensing of Environment, 112(11), 4009-4019. https://doi.org/10.1016/j.rse.2008.06.002
• [28] Ravishankar, T., Anil, T. C., Verma, U., Pai, M. M. M., & Pai. R. (2022). MartiNet: An efficient approach for river segmentation in SAR images. IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) (pp. 1-6). IEEE. https://doi.org/10.1109/CONECCT55679.2022.9865830
• [29] Rodríguez-Benito, C. V., Navarro, G., & Caballero, I. (2020). Using Copernicus Sentinel-2 and Sentinel-3 data to monitor harmful algal blooms in Southern Chile during the COVID-19 lockdown. Marine Pollution Bulletin, 161(Part A), 111722. https://doi.org/10.1016/j.marpolbul.2020.111722
• [30] Roelke, D., & Buyukates, Y. (2001). The Diversity of harmful algal bloom-triggering mechanisms and the complexity of bloom initiation. Human and Ecological Risk Assessment: An International Journal, 7(5), 1347-1362. https://doi.org/10.1080/20018091095041
• [31] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmen-tation. In N. Navab, J. Hornegger, W. M. Wells, & A. F. Frangi (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (Vol. 9351, pp. 234–241). Springer International Pu-blishing. https://doi.org/10.1007/978-3-319-24574-4_28
• [32] Shelhamer, E., Long, J., & Darrell, T. (2017). Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. https://doi.org/10.1109/TPAMI.2016.2572683
• [33] Singh, N. J., & Nongmeikapam, K. (2023). Semantic segmentation of satellite images using Deep-Unet. Arabian Journal for Science and Engineering, 48, 1193–1205. https://doi.org/10.1007/s13369-022-06734-4
• [34] Srichandan, S., Baliarsingh, S. K., Samanta, A. Jena, A. K., Lotliker, A. A., Nair, T. M. B., Barik, K. K., & Acharyya, T. (2022). Satellite-based characterization of phytoplankton blooms in coastal waters of the northwestern bay of bengal. Journal of the Indian Society of Remote Sensing, 50, 2221-2228. https://doi.org/10.1007/s12524-022-01597-6
• [35] Tendolkar, A., Choraria, M. M., Manohara Pai, S., Girisha, G., Dsouza & Adithya, K. S. (2021). Modified crop health monitoring and pesticide spraying system using NDVI and Semantic Segmentation: An AGROCOPTER based approach. IEEE International Conference on Autonomous Systems (ICAS) (pp. 1-5). IEEE. https://doi.org/10.1109/ICAS49788.2021.9551116
• [36] Tholkapiyan, M., Shanmugam, P., & Suresh, T. (2014). Monitoring of ocean surface algal blooms in coastal and oceanic waters around India. Environmental Monitoring and Assessment, 186, 4129–4137. https://doi.org/10.1007/s10661-014-3685-x
• [37] Vase, V. K., Ajay, N., Kumar, R. Jayaraman, J., & Rohit, P. (2022). Evaluation of satellite sensors to compute Chlorophyll-a concentration in the Northeastern Arabian Sea: A validation approach. Journal of the Indian Society of Remote Sensing, 50, 2209-2220. https://doi.org/10.1007/s12524-022-01598-5
• [38] Verma, U., Chauhan, A., Manohara, M.P., & Pai, R. (2021). DeepRivWidth: Deep Learning based semantic segmentation approach for river identification and width measurement in SAR images of coastal Karnataka. Computers & Geosciences, 154, 104805. https://doi.org/10.1016/j.cageo.2021.104805
• [39] Wang, Z., Zhang, S., Zhang, C., & Wang, B. (2023). Hidden feature-guided semantic segmentation network for remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-17, 5603417. https://doi.org/10.1109/TGRS.2023.3244273
• [40] Yang, N., & Tang, H. (2021). Semantic segmentation of satellite images: A Deep Learning approach integrated with geospatial hash codes. Remote Sensing, 13(14), 2723. https://doi.org/10.3390/rs13142723
• [41] Zhu, S., Wu, Y., & Ma, X. (2023). Deep Learning-based algal bloom identification method from remote sensing images - Take China’s Chaohu Lake as an example. Sustainability, 15(5), 4545. https://doi.org/10.3390/su15054545

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).