Abstract
In this paper, a deep learning classification model is proposed for automatically detecting the marine oil spill in Lanset-7 and Lanset-8 images, which can combine fully convolutional network (FCN) with Resnet and Googlenet respectively. The classification algorithms, i.e. FCN-Googlenet and FCN-ResNet are compared to the state-of-the-art Support Vector Machine (SVM) method. The experimental results show that our FCN-Googlenet and FCN-ResNet models outperform other approaches with a significant improvement. Moreover, our methods are more flexible in that no restriction on the size of input image is required in our algorithmic setups, which is more suitable in real applications.
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Acknowledgments
This work has been supported in part by Shenzhen Science and Technology Program under Grant no.JCYJ20160413163534712, J-CYJ20160428092427867, SGG20150512145714247 and JSGG20160229154017074.
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Li, Y. et al. (2018). Detection of Oil Spill Through Fully Convolutional Network. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_38
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