Abstract
Oil spills pose a threat and damage marine life and human beings. The illegal discharges of oil spills into the environment create an imbalance in the ecosystem. These oil spills occur due to releasing of oil residues from tankers, ballasts, accidents, or any human interventions. The oil spill detection studies emphasize the classification of oil spill images, detection of dark spots, and separation of trivial objects in oil spill images. The paper presents a study of deep learning technologies such as the traditional state-of-the-art architecture, convolutional neural network (CNN), VGG-16, VGG-19, inception V3, ResNet-50, and Xception to classify the oil spill dataset into two categories–oil spill and non-oil spills using the cross-validation method, Stratified KFold validation. Then, we evaluated the models using the Stratified KFold validation for 5, 10, 15, and 20 splits for 100 epochs, respectively. After performing the experiments, the ResNet-50 model gave the best score of 100% consistently for 5, 10, 15, and 20 splits, respectively.
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References
GVII, PSWG. Oil spill detection using RBF neural networks and SAR Data
Charmisha KS, Sowmya V, Soman KP (2018) Dimensionally reduced features for hyperspectral image classification using deep learning. In: International conference on communications and cyber physical engineering. Springer, Singapore, pp 171–179
Rahul-Vigneswaran K, Sachin-Kumar S, Mohan N, Soman KP (2019) Dynamic mode decomposition based feature for image classification. In: TENCON 2019—2019 IEEE region 10 conference (TENCON). IEEE, pp 745–750
Megha P, Sowmya V, Soman KP (2018) Effect of dynamic mode decomposition-based dimension reduction technique on hyperspectral image classification. In: Computational signal processing and analysis. Springer, Singapore, pp 89–99
Saiharsha B, Diwakar B, Karthika R, Ganesan M (2020) Evaluating performance of deep learning architectures for image classification. In: 2020 5th international conference on communication and electronics systems (ICCES). IEEE, pp 917–922
Krestenitis M, Orfanidis G, Ioannidis K, Avgerinakis K, Vrochidis S, Kompatsiaris I (2019) Oil spill identification from satellite images using deep neural networks. Remote Sens 11(15):1762
Topouzelis KN (2008) Oil spill detection by SAR images: dark formation detection, feature extraction and classification algorithms. Sensors 8(10):6642–6659. https://doi.org/10.3390/s8106642
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Gopinath, V., Sachin Kumar, S., Mohan, N., Soman, K.P. (2023). Oil Spill Detection from Images Using Deep Learning. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds) Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_65
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DOI: https://doi.org/10.1007/978-981-99-3656-4_65
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