Oil Spill Detection Based on Multiscale Multidimensional Residual CNN for Optical Remote Sensing Imagery
Oil spill (OS), as one of the main pollutions in the ocean, is a serious threat to the marine environment. Thus, timely and accurate OS detection (OSD) is necessary for ocean management. In this regard, remote sensing (RS) plays a key role due to multiple advantages over large and remote ocean envir...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/3907b0bbefc643cf8a7a8a855acf33f2 |
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| Sumario: | Oil spill (OS), as one of the main pollutions in the ocean, is a serious threat to the marine environment. Thus, timely and accurate OS detection (OSD) is necessary for ocean management. In this regard, remote sensing (RS) plays a key role due to multiple advantages over large and remote ocean environments. In this study, a new OSD framework based on a deep learning algorithm was developed for optical RS imagery. The proposed method was based on a multiscale multidimensional residual kernel convolution neural network. The proposed method investigated the deep features by the two-dimensional multiscale residual blocks and, then, utilized them at one-dimensional multiscale residual blocks. In this study, Landsat-5 satellite imagery acquired over the Gulf of Mexico was applied to evaluate the performance of the proposed method. The overall accuracy of the proposed method was more than 95%, and the miss detection and false alarm rates were less than 5%, indicating its high potential for OSD. Moreover, it was observed that the proposed method had better performance compared to other OSD algorithms that were investigated in this study. |
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