Research Article
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Year 2023, Volume: 36 Issue: 2, 592 - 606, 01.06.2023
https://doi.org/10.35378/gujs.973082

Abstract

References

  • [1] Sun, Z., “A novel approach to coral fish detection and classification in underwater footage based on convolutional neural network”, 2020 International Conference on Applied Physics and Computing (ICAPC 2020), Ottawa, Canada, 1-8, (2020).
  • [2] Corrales, X., Vilas, D., Piroddi, C., Steenbeek, J., Claudet, J., Lloret, J., Calò, A., Di Franco, A., Font, T., Ligas, A., Prato, G., Sahyoun, R., Sartor, P., Guidetti, P., Coll, M., “Multi-zone marine protected areas: Assessment of ecosystem and fisheries benefits using multiple ecosystem models”, Ocean & Coastal Management, 193(8): 1-12, (2020).
  • [3] Shihavuddin, A.S.M., Gracias, N., Garcia, R., Gleason, A.C.R., Gintert, B., “Image-based coral reef classification and thematic mapping”, Remote Sensing, 5(4): 1809–1841, (2013).
  • [4] Chegoonian, A.M., Mokhtarzade, M., Valadon Zoej, M.J., Salehi, M., “Soft supervised classification: An improved method for coral reef classification using medium resolution satellite images”, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2787–2790, (2016).
  • [5] Politikos, D. V., Fakiris, E., Davvetas, A., Klampanos, I.A., Papatheodorou, G., “Automatic detection of seafloor marine litter using towed camera images and deep learning”, Marine Pollution Bulletin, 164(5): 1-10, (2021).
  • [6] Ariyasu, E., Kakuta, S., Goto, K., Sano, T., “Evaluation of coral reefs mapping in kerama islands by satellite-based classification”, 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), Yokohama, Japan, 2670–2673, (2019).
  • [7] Purkis, S.J., Gleason, A.C.R., Purkis, C.R., Dempsey, A.C., Renaud, P.G., Faisal, M., Saul, S., Kerr, J.M., “High-resolution habitat and bathymetry maps for 65,000 sq. km of Earth’s remotest coral reefs”, Coral Reefs, 38(3): 467–488, (2019).
  • [8] Hopkinson, B.M., King, A.C., Owen, D.P., Johnson-Roberson, M., Long, M.H., Bhandarkar, S.M., “Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks”, PLoS One, 15(3): e0230671, (2020).
  • [9] Diegues, A., Pinto, J., Ribeiro, P., Frias, R., Alegre, D.C., “Automatic habitat mapping using convolutional neural networks”, 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV), Porto, Portugal, 1–6, (2018).
  • [10] Iqbal, K., Odetayo, M., James, A., Salam, R.A., Talib, A.Z.H., “Enhancing the low quality images using unsupervised colour correction method” 2010 IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey, 1703–1709, (2010).
  • [11] Mahmood, A., Bennamoun, M., An, S., Sohel, F., Boussaid, F., “ResFeats: Residual network based features for underwater image classification”, Image and Vision Computing, 93(1): 1–7, (2020).
  • [12] Pierce, J.P., Rzhanov, Y., Lowell, K., Dijkstra, J.A., “Reducing Annotation Times: Semantic Segmentation of Coral Reef Survey Images”, Global Oceans 2020, Singapore - U.S. Gulf Coast, 1-9, (2020).
  • [13] Kratsch, W., Manderscheid, J., Röglinger, M., Seyfried, J., “Machine learning in business process monitoring: A comparison of deep learning and classical approaches used for outcome prediction”, Business & Information Systems Engineering, 63(3): 261–276, (2021).
  • [14] Polak, P., Nelischer, C., Guo, H., Robertson, D.C., ““Intelligent” finance and treasury management: what we can expect”, AI & SOCIETY, 35(3): 715–726, (2020).
  • [15] Caballo, M., Pangallo, D.R., Mann, R.M., Sechopoulos, I., “Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence”, Computers in biology and medicine, 118103629, (2020).
  • [16] Pierson, H.A., Gashler, M.S., “Deep learning in robotics: a review of recent research”, Advanced Robotics, 31(16): 821–835, (2017).
  • [17] Schubert, J., Brynielsson, J., Nilsson, M., Svenmarck, P., “Artificial intelligence for decision support in command and control systems”, 23rd International Command and Control Research and Technology Symposium, Multi-Domain C, Stockholm, 1-17, (2018).
  • [18] Hassan, M.D., Nejdet N.A., Baker, M.R., Mahmood, S., “Enhancement automatic speech recognition by deep neural networks”, Periodicals of Engineering and Natural Sciences, 9(4): 921–927, (2021).
  • [19] Yasir, M., Rahman, A.U., Gohar, M., “Habitat mapping using deep neural networks”, Multimedia Systems, 27(4): 679–690, (2021).
  • [20] Nadeem, U., Bennamoun, M., Sohel, F., Togneri, R., “Deep fusion net for coral classification in fluorescence and reflectance images”, Digital Image Computing: Techniques and Applications, DICTA 2019, Perth, 1-7, (2019).
  • [21] Beijbom, O., Edmunds, P.J., Kline, D.I., Mitchell, B.G., Kriegman, D., “Automated annotation of coral reef survey images”, 2012 IEEE Conference On Computer Vision and Pattern Recognition (CVPR), Providence, USA , 1170–1177, (2012).
  • [22] Pizarro, O., Rigby, P., Johnson-Roberson, M., Williams, S.B., Colquhoun, J., “Towards image-based marine habitat classification”, OCEANS 2008, Quebec, Canada, 1–7, (2008).
  • [23] Mary, N.A.B., Dharma, D., “Coral reef image classification employing Improved LDP for feature extraction”, Journal of Visual Communication and Image Representation, 49(8): 225–242, (2017).
  • [24] Stokes, M.D., Deane, G.B., “Automated processing of coral reef benthic images”, Limnology and Oceanography: Methods, 7(2): 157–168, (2009).
  • [25] Sotoodeh, M., Moosavi, M.R., Boostani, R., “A structural based feature extraction for detecting the relation of hidden substructures in coral reef images”, Multimedia Tools and Applications, 78(24): 34513–34539, (2019).
  • [26] Shakoor, M.H., Boostani, R., “Noise robust and rotation invariant texture classification based on local distribution transform”, Multimedia Tools and Applications, 80(6): 8639–8666, (2021).
  • [27] He, K., Zhang, X., Ren, S., Sun, J., “Deep residual learning for image recognition”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 770–778, (2016).
  • [28] Deng, J., Dong, W., Socher, R., Li, J.L., Ki, K., Fei, L.F., “ImageNet: A large-scale hierarchical image database”, 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 248–255, (2009).
  • [29] Mahmood, A., Bennamoun, M., An, S., Sohel, F., Boussaid, F., Hovey, R., Kendrick, G., Fisher, R.B., “Coral classification with hybrid feature representations”, 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, USA, 519–523, (2016).
  • [30] Simonyan, K., Zisserman, A., “Very deep convolutional networks for large-scale image recognition”, III. International Conference on Learning Representations (ICLR 2015), San Diego, USA, 1409–1556, (2015).
  • [31] Mahmood, A., Bennamoun, M., An, S., Sohel, Boussaid, F., Hovey, R., Kendrick, G., Fisher, R.B., “Automatic annotation of coral reefs using deep learning”, OCEANS 2016 MTS/IEEE Monterey, Monterey, USA, 1–5, (2016).
  • [32] Bhardwaj, N., Kaur, G., and Singh, P.K., “A systematic review on image enhancement techniques”, Sensors and Image Processing, Springer, Singapore, 227–235, (2018).
  • [33] Duarte, A., Codevilla, F., Gaya, J.D.O., Botelho, S.S.C., “A dataset to evaluate underwater image restoration methods”, OCEANS 2016 – Shanghai, 1–6, (2016).
  • [34] Gao, F., Wang, K., Yang, Z., Wang, Y., Zhang, Q., “Underwater image enhancement based on local contrast correction and multi-scale fusion”, Journal of Marine Science and Engineering, 9(2): 1–16, (2021).
  • [35] Rizzi, A., Gatta, C., Marini, D., “A new algorithm for unsupervised global and local color correction”, Pattern Recognition Letters, 24(11): 1663–1677, (2003).
  • [36] Iqbal, K., Salam, R., Osman, A., and Of, A.T., “Underwater image enhancement using an integrated colour model”, IAENG International Journal of Computer Science, 32(2): 239–244, (2007).
  • [37] Kim, Y.T., “Contrast enhancement using brightness preserving bi-histogram equalization”, IEEE Transactions on Consumer Electronics, 43(1): 1–8, (1997).
  • [38] Zhang, W., Pan, X., Xie, X., Li, L., Wang, Z., Han, C., “Color correction and adaptive contrast enhancement for underwater image enhancement”, Computers & Electrical Engineering, 91(3): 1–14, (2021).
  • [39] Mahiddine, A., Seinturier, J., Boi, D.P.J.M., Drap, P., Merad, D., Long, L., “Underwater image preprocessing for automated photogrammetry in high turbidity water: An application on the Arles-Rhone XIII roman wreck in the Rhodano river France”, 2012 XVIII. International Conference on Virtual Systems and Multimedia, Milan, Italy, 189-194i, (2012).
  • [40] Mathur, M., Goel, N., “Enhancement algorithm for high visibility of underwater images”, IET Image Processing, 16(4): 1067-1082, (2022).
  • [41] Singh, P., Mukundan, R., De Ryke, R., “Feature enhancement in medical ultrasound videos using contrast-limited adaptive histogram equalization”, Journal of Digital Imaging, 33(1): 273–285, (2020).
  • [42] Zhu, Y., Huang, C., “An adaptive histogram equalization algorithm on the image gray level mapping”, Physics Procedia, 25: 601–608, (2012).
  • [43] Yadav, G., Maheshwari, S., Agarwal, A., “Contrast limited adaptive histogram equalization based enhancement for real time video system”, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Delhi, India, 2392–2397, (2014).
  • [44] Ancuti, C., Ancuti, C.O., Haber, T., Bekaert, P., “Enhancing underwater images and videos by fusion”, 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 81–88, (2012).
  • [45] Xu, G., Su, J., Pan, H., Zhang, Z., Gong, H., “An image enhancement method based on gamma correction”, 2009 Second International Symposium on Computational Intelligence and Design, Changsha, China, 60–63, (2009).
  • [46] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., “Densely connected convolutional networks”, 2017 IEEE Conference On Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2261–2269, (2017).
  • [47] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., “MobileNets: Efficient convolutional neural networks for mobile vision applications”, arXiv:1704.04861, (2017).

The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping

Year 2023, Volume: 36 Issue: 2, 592 - 606, 01.06.2023
https://doi.org/10.35378/gujs.973082

Abstract

Marine habitat mapping is primarily done to monitor and preserve underwater ecosystems. Images captured in a marine environment suffer from acidification, pollutions, waste chemicals, and lighting conditions. Human beings are progressing fast in terms of technology and are also responsible for the degradations of ecosystems, both marine and land habitats. Marine biologists possess a lot of data for the underwater environment, but it is hard to analyze, and the task becomes tiresome. Automating this process would help marine biologists quickly monitor the environment and preserve it. Our research focuses on coral reef classification and two critical aspects, i.e., Image enhancement and recognition of coral reefs. Image enhancement plays an essential role in marine habitat mapping because of the environment in which images are taken. The literature contains many image enhancement techniques for underwater. The authors want to determine whether a single image enhancement technique is suitable for coral reefs. Four image enhancement techniques based on an extensive literature review are selected. We have used DenseNet-169 and MobileNet for image classification. It has been reported that DenseNet-169 has excellent results for coral reefs classification. Histogram techniques combined with DenseNet-169 for classification resulted in higher classification rates. 

References

  • [1] Sun, Z., “A novel approach to coral fish detection and classification in underwater footage based on convolutional neural network”, 2020 International Conference on Applied Physics and Computing (ICAPC 2020), Ottawa, Canada, 1-8, (2020).
  • [2] Corrales, X., Vilas, D., Piroddi, C., Steenbeek, J., Claudet, J., Lloret, J., Calò, A., Di Franco, A., Font, T., Ligas, A., Prato, G., Sahyoun, R., Sartor, P., Guidetti, P., Coll, M., “Multi-zone marine protected areas: Assessment of ecosystem and fisheries benefits using multiple ecosystem models”, Ocean & Coastal Management, 193(8): 1-12, (2020).
  • [3] Shihavuddin, A.S.M., Gracias, N., Garcia, R., Gleason, A.C.R., Gintert, B., “Image-based coral reef classification and thematic mapping”, Remote Sensing, 5(4): 1809–1841, (2013).
  • [4] Chegoonian, A.M., Mokhtarzade, M., Valadon Zoej, M.J., Salehi, M., “Soft supervised classification: An improved method for coral reef classification using medium resolution satellite images”, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2787–2790, (2016).
  • [5] Politikos, D. V., Fakiris, E., Davvetas, A., Klampanos, I.A., Papatheodorou, G., “Automatic detection of seafloor marine litter using towed camera images and deep learning”, Marine Pollution Bulletin, 164(5): 1-10, (2021).
  • [6] Ariyasu, E., Kakuta, S., Goto, K., Sano, T., “Evaluation of coral reefs mapping in kerama islands by satellite-based classification”, 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), Yokohama, Japan, 2670–2673, (2019).
  • [7] Purkis, S.J., Gleason, A.C.R., Purkis, C.R., Dempsey, A.C., Renaud, P.G., Faisal, M., Saul, S., Kerr, J.M., “High-resolution habitat and bathymetry maps for 65,000 sq. km of Earth’s remotest coral reefs”, Coral Reefs, 38(3): 467–488, (2019).
  • [8] Hopkinson, B.M., King, A.C., Owen, D.P., Johnson-Roberson, M., Long, M.H., Bhandarkar, S.M., “Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks”, PLoS One, 15(3): e0230671, (2020).
  • [9] Diegues, A., Pinto, J., Ribeiro, P., Frias, R., Alegre, D.C., “Automatic habitat mapping using convolutional neural networks”, 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV), Porto, Portugal, 1–6, (2018).
  • [10] Iqbal, K., Odetayo, M., James, A., Salam, R.A., Talib, A.Z.H., “Enhancing the low quality images using unsupervised colour correction method” 2010 IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey, 1703–1709, (2010).
  • [11] Mahmood, A., Bennamoun, M., An, S., Sohel, F., Boussaid, F., “ResFeats: Residual network based features for underwater image classification”, Image and Vision Computing, 93(1): 1–7, (2020).
  • [12] Pierce, J.P., Rzhanov, Y., Lowell, K., Dijkstra, J.A., “Reducing Annotation Times: Semantic Segmentation of Coral Reef Survey Images”, Global Oceans 2020, Singapore - U.S. Gulf Coast, 1-9, (2020).
  • [13] Kratsch, W., Manderscheid, J., Röglinger, M., Seyfried, J., “Machine learning in business process monitoring: A comparison of deep learning and classical approaches used for outcome prediction”, Business & Information Systems Engineering, 63(3): 261–276, (2021).
  • [14] Polak, P., Nelischer, C., Guo, H., Robertson, D.C., ““Intelligent” finance and treasury management: what we can expect”, AI & SOCIETY, 35(3): 715–726, (2020).
  • [15] Caballo, M., Pangallo, D.R., Mann, R.M., Sechopoulos, I., “Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence”, Computers in biology and medicine, 118103629, (2020).
  • [16] Pierson, H.A., Gashler, M.S., “Deep learning in robotics: a review of recent research”, Advanced Robotics, 31(16): 821–835, (2017).
  • [17] Schubert, J., Brynielsson, J., Nilsson, M., Svenmarck, P., “Artificial intelligence for decision support in command and control systems”, 23rd International Command and Control Research and Technology Symposium, Multi-Domain C, Stockholm, 1-17, (2018).
  • [18] Hassan, M.D., Nejdet N.A., Baker, M.R., Mahmood, S., “Enhancement automatic speech recognition by deep neural networks”, Periodicals of Engineering and Natural Sciences, 9(4): 921–927, (2021).
  • [19] Yasir, M., Rahman, A.U., Gohar, M., “Habitat mapping using deep neural networks”, Multimedia Systems, 27(4): 679–690, (2021).
  • [20] Nadeem, U., Bennamoun, M., Sohel, F., Togneri, R., “Deep fusion net for coral classification in fluorescence and reflectance images”, Digital Image Computing: Techniques and Applications, DICTA 2019, Perth, 1-7, (2019).
  • [21] Beijbom, O., Edmunds, P.J., Kline, D.I., Mitchell, B.G., Kriegman, D., “Automated annotation of coral reef survey images”, 2012 IEEE Conference On Computer Vision and Pattern Recognition (CVPR), Providence, USA , 1170–1177, (2012).
  • [22] Pizarro, O., Rigby, P., Johnson-Roberson, M., Williams, S.B., Colquhoun, J., “Towards image-based marine habitat classification”, OCEANS 2008, Quebec, Canada, 1–7, (2008).
  • [23] Mary, N.A.B., Dharma, D., “Coral reef image classification employing Improved LDP for feature extraction”, Journal of Visual Communication and Image Representation, 49(8): 225–242, (2017).
  • [24] Stokes, M.D., Deane, G.B., “Automated processing of coral reef benthic images”, Limnology and Oceanography: Methods, 7(2): 157–168, (2009).
  • [25] Sotoodeh, M., Moosavi, M.R., Boostani, R., “A structural based feature extraction for detecting the relation of hidden substructures in coral reef images”, Multimedia Tools and Applications, 78(24): 34513–34539, (2019).
  • [26] Shakoor, M.H., Boostani, R., “Noise robust and rotation invariant texture classification based on local distribution transform”, Multimedia Tools and Applications, 80(6): 8639–8666, (2021).
  • [27] He, K., Zhang, X., Ren, S., Sun, J., “Deep residual learning for image recognition”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 770–778, (2016).
  • [28] Deng, J., Dong, W., Socher, R., Li, J.L., Ki, K., Fei, L.F., “ImageNet: A large-scale hierarchical image database”, 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 248–255, (2009).
  • [29] Mahmood, A., Bennamoun, M., An, S., Sohel, F., Boussaid, F., Hovey, R., Kendrick, G., Fisher, R.B., “Coral classification with hybrid feature representations”, 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, USA, 519–523, (2016).
  • [30] Simonyan, K., Zisserman, A., “Very deep convolutional networks for large-scale image recognition”, III. International Conference on Learning Representations (ICLR 2015), San Diego, USA, 1409–1556, (2015).
  • [31] Mahmood, A., Bennamoun, M., An, S., Sohel, Boussaid, F., Hovey, R., Kendrick, G., Fisher, R.B., “Automatic annotation of coral reefs using deep learning”, OCEANS 2016 MTS/IEEE Monterey, Monterey, USA, 1–5, (2016).
  • [32] Bhardwaj, N., Kaur, G., and Singh, P.K., “A systematic review on image enhancement techniques”, Sensors and Image Processing, Springer, Singapore, 227–235, (2018).
  • [33] Duarte, A., Codevilla, F., Gaya, J.D.O., Botelho, S.S.C., “A dataset to evaluate underwater image restoration methods”, OCEANS 2016 – Shanghai, 1–6, (2016).
  • [34] Gao, F., Wang, K., Yang, Z., Wang, Y., Zhang, Q., “Underwater image enhancement based on local contrast correction and multi-scale fusion”, Journal of Marine Science and Engineering, 9(2): 1–16, (2021).
  • [35] Rizzi, A., Gatta, C., Marini, D., “A new algorithm for unsupervised global and local color correction”, Pattern Recognition Letters, 24(11): 1663–1677, (2003).
  • [36] Iqbal, K., Salam, R., Osman, A., and Of, A.T., “Underwater image enhancement using an integrated colour model”, IAENG International Journal of Computer Science, 32(2): 239–244, (2007).
  • [37] Kim, Y.T., “Contrast enhancement using brightness preserving bi-histogram equalization”, IEEE Transactions on Consumer Electronics, 43(1): 1–8, (1997).
  • [38] Zhang, W., Pan, X., Xie, X., Li, L., Wang, Z., Han, C., “Color correction and adaptive contrast enhancement for underwater image enhancement”, Computers & Electrical Engineering, 91(3): 1–14, (2021).
  • [39] Mahiddine, A., Seinturier, J., Boi, D.P.J.M., Drap, P., Merad, D., Long, L., “Underwater image preprocessing for automated photogrammetry in high turbidity water: An application on the Arles-Rhone XIII roman wreck in the Rhodano river France”, 2012 XVIII. International Conference on Virtual Systems and Multimedia, Milan, Italy, 189-194i, (2012).
  • [40] Mathur, M., Goel, N., “Enhancement algorithm for high visibility of underwater images”, IET Image Processing, 16(4): 1067-1082, (2022).
  • [41] Singh, P., Mukundan, R., De Ryke, R., “Feature enhancement in medical ultrasound videos using contrast-limited adaptive histogram equalization”, Journal of Digital Imaging, 33(1): 273–285, (2020).
  • [42] Zhu, Y., Huang, C., “An adaptive histogram equalization algorithm on the image gray level mapping”, Physics Procedia, 25: 601–608, (2012).
  • [43] Yadav, G., Maheshwari, S., Agarwal, A., “Contrast limited adaptive histogram equalization based enhancement for real time video system”, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Delhi, India, 2392–2397, (2014).
  • [44] Ancuti, C., Ancuti, C.O., Haber, T., Bekaert, P., “Enhancing underwater images and videos by fusion”, 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 81–88, (2012).
  • [45] Xu, G., Su, J., Pan, H., Zhang, Z., Gong, H., “An image enhancement method based on gamma correction”, 2009 Second International Symposium on Computational Intelligence and Design, Changsha, China, 60–63, (2009).
  • [46] Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., “Densely connected convolutional networks”, 2017 IEEE Conference On Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2261–2269, (2017).
  • [47] Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., “MobileNets: Efficient convolutional neural networks for mobile vision applications”, arXiv:1704.04861, (2017).
There are 47 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Computer Engineering
Authors

Ehab Shaker This is me 0000-0002-4098-6818

Mohammed Rashad Baker 0000-0001-6986-4921

Zuhair Mahmood This is me 0000-0002-2268-9182

Publication Date June 1, 2023
Published in Issue Year 2023 Volume: 36 Issue: 2

Cite

APA Shaker, E., Baker, M. R., & Mahmood, Z. (2023). The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping. Gazi University Journal of Science, 36(2), 592-606. https://doi.org/10.35378/gujs.973082
AMA Shaker E, Baker MR, Mahmood Z. The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping. Gazi University Journal of Science. June 2023;36(2):592-606. doi:10.35378/gujs.973082
Chicago Shaker, Ehab, Mohammed Rashad Baker, and Zuhair Mahmood. “The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping”. Gazi University Journal of Science 36, no. 2 (June 2023): 592-606. https://doi.org/10.35378/gujs.973082.
EndNote Shaker E, Baker MR, Mahmood Z (June 1, 2023) The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping. Gazi University Journal of Science 36 2 592–606.
IEEE E. Shaker, M. R. Baker, and Z. Mahmood, “The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping”, Gazi University Journal of Science, vol. 36, no. 2, pp. 592–606, 2023, doi: 10.35378/gujs.973082.
ISNAD Shaker, Ehab et al. “The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping”. Gazi University Journal of Science 36/2 (June 2023), 592-606. https://doi.org/10.35378/gujs.973082.
JAMA Shaker E, Baker MR, Mahmood Z. The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping. Gazi University Journal of Science. 2023;36:592–606.
MLA Shaker, Ehab et al. “The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping”. Gazi University Journal of Science, vol. 36, no. 2, 2023, pp. 592-06, doi:10.35378/gujs.973082.
Vancouver Shaker E, Baker MR, Mahmood Z. The Impact of Image Enhancement and Transfer Learning Techniques on Marine Habitat Mapping. Gazi University Journal of Science. 2023;36(2):592-606.