Bibliography

Bibliography#

1: Seyed Babak Haji Seyed Asadollah, Najeebullah Khan, Ahmad Sharafati, Shamsuddin Shahid, Eun Sung Chung, and Xiao Jun Wang. Prediction of heat waves using meteorological variables in diverse regions of iran with advanced machine learning models. Stochastic Environmental Research and Risk Assessment, 2022. doi:10.1007/s00477-021-02103-z.
2: N. Baker. William thompson – the world's first underwater photographer. Historical Diving Times, 1997.
3: Marco Balsi, Monica Moroni, Valter Chiarabini, and Giovanni Tanda. High-resolution aerial detection of marine plastic litter by hyperspectral sensing. Remote Sensing, 13(8):1557, 2021.
4: Cigdem Beyan and Howard I. Browman. Setting the stage for the machine intelligence era in marine science. ICES Journal of Marine Science, 2020. doi:10.1093/icesjms/fsaa084.
5: M Bhanumathi, R Gugan, and others. Marine plastic detection using deep learning. Advances in Parallel Computing Algorithms, Tools and Paradigms, pages 406–413, 2022.
6: John A Burns, Kaitlyn P Becker, David Casagrande, Joost Daniels, Paul Roberts, Eric Orenstein, Daniel M Vogt, Zhi Ern Teoh, Ryan Wood, Alexander H Yin, and others. An in situ digital synthesis strategy for the discovery and description of ocean life. Science Advances, 10(3):eadj4960, 2024.
7: Markus Diesing, Sophie L Green, David Stephens, R Murray Lark, Heather A Stewart, and Dayton Dove. Mapping seabed sediments: comparison of manual, geostatistical, object-based image analysis and machine learning approaches. Continental Shelf Research, 84:107–119, 2014.
8: Jennifer M. Durden, Timm Schoening, Franziska Althaus, Ariell Friedman, Rafael Garcia, Adrian G. Glover, Jens Greinert, Nancy Jacobsen Stout, Daniel O.B. Jones, Anne Jordt, Jeffrey W. Kaeli, Kevin Köser, Linda A. Kuhnz, Dhugal Lindsay, Kirsty J. Morris, Tim W. Nattkemper, Jonas Osterloff, Henry A. Ruhl, Hanumant Singh, Maggie Tran, and Brian J. Bett. Perspectives in visual imaging for marine biology and ecology: from acquisition to understanding. Oceanography and Marine Biology: An Annual Review, 2016. doi:10.1201/9781315368597.
9: Ying gang Zheng, Hong sheng Zhang, Kai tuo Qi, and Long yu Ding. Stripe segmentation of oceanic internal waves in sar images based on segnet. Geocarto International, 2022. doi:10.1080/10106049.2021.2002430.
10: Yunhao Gao, Feng Gao, Junyu Dong, and Shengke Wang. Transferred deep learning for sea ice change detection from synthetic-aperture radar images. IEEE Geoscience and Remote Sensing Letters, 2019. doi:10.1109/LGRS.2019.2906279.
11: Bernabe Gomez and Usama Kadri. Earthquake source characterization by machine learning algorithms applied to acoustic signals. Scientific Reports, 11(1):23062, 2021.
12: Yoo Geun Ham, Jeong Hwan Kim, and Jing Jia Luo. Deep learning for multi-year enso forecasts. Nature, 2019. doi:10.1038/s41586-019-1559-7.
13: Ali K Ibrahim, Hanqi Zhuang, Laurent M Chérubin, Nurgun Erdol, Gregory O'Corry-Crowe, and Ali Muhamed Ali. A multimodel deep learning algorithm to detect north atlantic right whale up-calls. The Journal of the Acoustical Society of America, 150(2):1264–1272, 2021.
14: Alan J. Jamieson, Ben Boorman, and Daniel O.B. Jones. Deep-sea benthic sampling. Methods for the Study of Marine Benthos, 2013. doi:10.1002/9781118542392.ch7.
15: Sihun Jung, Young Jun Kim, Sumin Park, and Jungho Im. Prediction of sea surface temperature and detection of ocean heat wave in the south sea of korea using time-series deep-learning approaches. Korean Journal of Remote Sensing, 2020. doi:10.7780/kjrs.2020.36.5.3.7.
16: Donna M Kocak and Frank M Caimi. The current art of underwater imaging–with a glimpse of the past and vision of the future. Marine Technology Society Journal, 39(3):5–26, 2005.
17: Jianfei Liu, William J Emery, Xiongbin Wu, Miao Li, Chuan Li, and Lan Zhang. Computing coastal ocean surface currents from modis and viirs satellite imagery. Remote Sensing, 9(10):1083, 2017.
18: Delphine Mallet and Dominique Pelletier. Underwater video techniques for observing coastal marine biodiversity: a review of sixty years of publications (1952-2012). Fisheries Research, 2014. doi:10.1016/j.fishres.2014.01.019.
19: Aaron Marburg and Katie Bigham. Deep learning for benthic fauna identification. OCEANS 2016 MTS/IEEE Monterey, pages 1–5, 2016. doi:10.1109/OCEANS.2016.7761146.
20: Andreas Marouchos, Matthew Sherlock, and Jeff Cordell. Challenges in underwater image capture. IEEE, pages 1–5, 2018.
21: Xiaoyi Pan, Jing Wang, Xudong Zhang, Yuan Mei, Lu Shi, and Guoqiang Zhong. A deep-learning model for the amplitude inversion of internal waves based on optical remote-sensing images. International Journal of Remote Sensing, 2018. doi:10.1080/01431161.2017.1390269.
22: Nabil Panchi, Ekaterina Kim, and Anirban Bhattacharyya. Supplementing remote sensing of ice: deep learning-based image segmentation system for automatic detection and localization of sea-ice formations from close-range optical images. IEEE Sensors Journal, 2021. doi:10.1109/JSEN.2021.3084556.
23: Nils Piechaud, Christopher Hunt, Phil F Culverhouse, Nicola L Foster, and Kerry L Howell. Automated identification of benthic epifauna with computer vision. Marine Ecology Progress Series, 615:15–30, 2019.
24: Peter Rubbens, Stephanie Brodie, Tristan Cordier, Diogo Destro Barcellos, Paul Devos, Jose A. Fernandes-Salvador, Jennifer I. Fincham, Alessandra Gomes, Nils Olav Handegard, Kerry Howell, Cédric Jamet, Kyrre Heldal Kartveit, Hassan Moustahfid, Clea Parcerisas, Dimitris Politikos, Raphaëlle Sauzède, Maria Sokolova, Laura Uusitalo, Laure Van Den Bulcke, Aloysius T.M. Van Helmond, Jordan T. Watson, Heather Welch, Oscar Beltran-Perez, Samuel Chaffron, David S. Greenberg, Bernhard Kühn, Rainer Kiko, Madiop Lo, Rubens M. Lopes, Klas Ove Möller, William Michaels, Ahmet Pala, Jean Baptiste Romagnan, Pia Schuchert, Vahid Seydi, Sebastian Villasante, Ketil Malde, and Jean Olivier Irisson. Machine learning in marine ecology: an overview of techniques and applications. ICES Journal of Marine Science, 2023. doi:10.1093/icesjms/fsad100.
25: Timm Schoening, Jennifer M Durden, Claas Faber, Janine Felden, Karl Heger, Henk-Jan T Hoving, Rainer Kiko, Kevin Köser, Christopher Krämmer, Tom Kwasnitschka, and others. Making marine image data fair. Scientific data, 9(1):414, 2022.
26: Timm Schoening, Jonas Osterloff, and Tim W. Nattkemper. Recomia-recommendations for marine image annotation: lessons learned and future directions. Frontiers in Marine Science, 2016. doi:10.3389/fmars.2016.00059.
27: Chris J. Smith and Heye Rumohr. Imaging techniques. Methods for the Study of Marine Benthos, 2013. doi:10.1002/9781118542392.ch3.
28: Tao Song, Cong Pang, Boyang Hou, Guangxu Xu, Junyu Xue, Handan Sun, and Fan Meng. A review of artificial intelligence in marine science. Frontiers in Earth Science, 2023. doi:10.3389/feart.2023.1090185.
29: Orkhan V. Valikhanli. The visual digital turn: using neural networks to study historical images. Problems of Information Technology, 2023. doi:10.25045/jpit.v14.i1.02.
30: Daniele Ventura, Andrea Bonifazi, Maria Flavia Gravina, Andrea Belluscio, and Giandomenico Ardizzone. Mapping and classification of ecologically sensitive marine habitats using unmanned aerial vehicle (uav) imagery and object-based image analysis (obia). Remote Sensing, 10(9):1331, 2018.
31: Marinos Vlachos and Dimitrios Skarlatos. An extensive literature review on underwater image colour correction. Sensors, 21(17):5690, 2021.
32: Melvin Wevers and Thomas Smits. The visual digital turn: using neural networks to study historical images. Digital Scholarship in the Humanities, 2019. doi:10.1093/llc/fqy085.
33: Min Ye, Jie Nie, Anan Liu, Zhigang Wang, Lei Huang, Hao Tian, Dehai Song, and Zhiqiang Wei. Multi-year enso forecasts using parallel convolutional neural networks with heterogeneous architecture. Frontiers in Marine Science, 2021. doi:10.3389/fmars.2021.717184.