Page 51 - AJWEP-v22i3
P. 51

SpillNet CNN model for oil spill detection

                   spill classification based on satellite image using deep   2023;116:103153.
                   learning techniques. Baghdad Sci.J. 2024;21(2(SI):0684.  39.  Bui NA, Oh Y, Lee I. Oil spill detection and classification
                27.  Dehghani-Dehcheshmeh  S,  Akhoondzadeh   M,        through deep learning and tailored data augmentation.
                   Homayouni S. Oil spills detection  from SAR  Earth   Int J Appl Earth Observ Geoinform. 2024;129:103845.
                   observations  based on a  hybrid  CNN transformer      doi: 10.1016/j.jag.2024.103845
                   networks. Mar Pollut Bull. 2023;190:114834.      40.  Ukpaka CP, Puyate YT, Nwokide LC. Predictive model
                28.  Kalyan  KS. A  survey of GPT-3 family  large language   to detect insulation failure and pipe leakage in natural
                   models including ChatGPT and GPT-4. Rochester, NY:   gas transmission pipeline  using simulation  software.
                   Cornell University, New York; 2023.                  Indian J Eng. 2019;16:135-166.
                   doi: 10.2139/ssrn.4593895                        41.  Ukenedo OG, Ukpaka CP, Nkoi B. Effects of unsafe acts
                29.  Urolagin S, Nayak J, Acharya UR. Gabor CNN based   and conditions on the reliability of equipment installation
                   intelligent  system  for visual  sentiment  analysis  of   in oil and gas servicing unit: A Case Study. Indian J Eng.
                   social media data on cloud environment. IEEE Access.   2022;19(51):294-309.
                   2022;10:132455-132471.                           42.  Khaira  A, Dwivedi  RK, Srivastava  S.  A  state  of the
                   doi: 10.1109/ACCESS.2022.3228263                     art  review of online  condition  monitoring  tools  using
                30.  Daniyan IA, Dahunsi OA, Oguntuase OB, Daniyan OL,   ndt as principal testing technique.  Indian J Eng.
                   Mpofu K. Development of a prototype test rig for leak   2016;13(33):338-346.
                   detection in pipelines. Procedia CIRP. 2019;80:524-529.  43.  Waghmare  SN, Raut DN, Mahajan SK, Bhamare  SS.
                   doi: 10.1016/j.procir.2019.01.016                    Improving reliability for SMES in India by using faults
                31.  Daniyan IA, Balogun V, Ererughurie OK, Daniyan OL,   classification. Indian J Eng. 2016;13(33):354-361.
                   Oladapo BI. Development of an inline inspection robot   44.  Promise NU, Ukpaka CP, Puyate  YT. Biokinetics  of
                   for the detection of pipeline defects. J Facilities Manag.   crude  oil  remediation  using Dogoyaro  (Azadirachta
                   2022;20(2):193-217.                                  indica) Stem. Indian J Eng. 2020;17(47):250-260.
                   doi: 10.1108/JFM-01-2021-0010                    45.  Das K, Janardhana P, Narayana H.  Application  of
                32.  Krestenitis M, Orfanidis G, Ioannidis K, Avgerinakis K,   CNN based image classification technique for oil spill
                   Vrochidis S, Kompatsiaris I. Oil spill identification from   detection. Indian J Geo Mar Sci. 2023;52(1):5-14.
                   satellite  images using deep  neural  networks.  Remote   46.  Guo H, Wu D, An J. Discrimination  of oil slicks and
                   Sens. 2019;11(15):1762.                              look-alikes  in polarimetric  SAR images  using CNN.
                   doi: 10.3390/rs11151762                              Sensors. 2017;17(8):1837.
                33.  Chollet F. Xception: Deep Learning with Depthwise      doi: 10.3390/s17081837
                   Separable  Convolutions,  2017 IEEE  Conference  on   47.  Hidalgo MN, Gallego AJ, Gil P, Pertusa A. Two-stage
                   Computer Vision and Pattern  Recognition  (CVPR),    convolutional neural network for ship and spill detection
                   Honolulu, HI, USA, 2017, pp. 1800-1807.              using SLAR images. IEEE Trans Geosci Remote Sens.
                   doi: 10.1109/CVPR.2017.195                           2018;56(9):5217-5230.
                34.  He K, Zhang X, Ren S, Sun J. Deep Residual Learning      doi: 10.1109/TGRS. 2018.2812619
                   for Image  Recognition,  2016 IEEE  Conference  on   48.  Cantorna D, Dafonte C, Iglesias A, Arcay B. Oil spill
                   Computer Vision and Pattern Recognition (CVPR), Las   segmentation  in SAR images  using convolutional
                   Vegas, NV, USA, 2016, pp. 770-778.                   neural networks. A comparative analysis with clustering
                   doi: 10.1109/CVPR.2016.90                            and logistic  regression algorithms.  Appl Soft Comput.
                35.  Mustafa  A, Kim  H, Hilton  A. MSFD: Multi-Scale   2019;84:105716.
                   segmentation-based feature detection for wide-baseline      doi: 10.1016/j.asoc.2019.105716
                   scene reconstruction.  IEEE  Trans Image Process.   49.  Zeng K, Wang Y. A deep convolutional neural network
                   2019;28(3):1118-1132.                                for oil spill detection  from spaceborne SAR  images.
                   doi: 10.1109/TIP.2018.2872906                        Remote Sens. 2020;12(6):1015.
                36.  Mera D, Bolon-Canedo V, Cotos JM, Alonso-Betanzos      doi: 10.3390/rs12061015
                   A. On the  use of feature  selection  to improve  the   50.  Song D, Zhen  Z,  Wang  B,  et  al.  A  novel  marine  oil
                   detection of sea oil spill in SAR images. Comput Geosci.   spillage  identification  scheme  based  on  convolution
                   2017;100:166-178.                                    neural network feature extraction from fully polarimetric
                37.  Najouri  Z,  Rianzanoff  S,  Defontaines  B,  Xavier  JP.   SAR imagery. IEEE Access. 2020;8:59801-59820.
                   A statistical  approach to preprocess and enhance      doi: 10.1109/ACCESS.2020.2979219
                   c-band  SAR images  in  order  to  detect  automatically   51.  Kang J, Yang C, Yi J, Lee Y. Detection of marine oil spill
                   marine oi slicks.  IEEE Trans Geosci Remote Sens.    from planetscope images  using CNN and transformer
                   2018;56:2554-2564.                                   models. J Mar Sci Eng. 2024;12:2095.
                38.  Liu X, Zhang Y, Zhou H, et al. Multi-source knowledge   52.  Hamza  MS, Jauro SS, Ismail  M. Oil  spill  detection
                   graph reasoning for ocean oil spill detection from satellite   using convolutional neural network. Bima J Sci Technol.
                   SAR images.  Int  J  Appl  Earth  Observ Geoinform.   2023;7(4):15-30.


                Volume 22 Issue 3 (2025)                        45                                 doi: 10.36922/ajwep.8282
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