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Author:

Zhang, Jin (Zhang, Jin.) | Feng, Hao (Feng, Hao.) | Luo, Qingli (Luo, Qingli.) | Li, Yu (Li, Yu.) | Zhang, Yu (Zhang, Yu.) | Li, Jian (Li, Jian.) | Zeng, Zhoumo (Zeng, Zhoumo.)

Indexed by:

EI Scopus SCIE

Abstract:

Synthetic aperture radar (SAR) has been widely applied in oil spill detection on the sea surface due to the advantages of wide area coverage, all-weather operation, and multi-polarization characteristics. Sentinel-1 satellites can provide dual-polarized SAR data, and they have high potential for successful application to oil spill detection. However, the characteristics of the sea surface and oil film on different images are not the same when imaging at different locations and in different conditions, which leads to the inconsistent accuracy of these images with the application of the current oil spill detection methods. In order to avoid the above limitation, we propose an oil spill detection method using image stretching based on superpixels and a convolutional neural network. Experiments were carried out on eight Sentinel-1 dual-pol data, and the optimal superpixel number and image stretching parameters are discussed. Mean intersection over union (MIoU) was used to evaluate classification accuracy. The proposed method could effectively improve the classification accuracy; when the expansion and inhibition coefficients of image stretching were set to 1.6 and 1.2 respectively, the experiments achieved a maximum MIoU of 85.4%, 7.3% higher than that without image stretching. © 2022 by the authors.

Keyword:

Image enhancement Surface waters Synthetic aperture radar Convolutional neural networks Image segmentation Oil spills Superpixels Convolution Radar imaging Deep neural networks

Author Community:

  • [ 1 ] [Zhang, Jin]State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin; 300072, China
  • [ 2 ] [Feng, Hao]State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin; 300072, China
  • [ 3 ] [Feng, Hao]Binhai International Advanced Structural Integrity Research Centre, Tianjin; 300072, China
  • [ 4 ] [Luo, Qingli]State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin; 300072, China
  • [ 5 ] [Luo, Qingli]Binhai International Advanced Structural Integrity Research Centre, Tianjin; 300072, China
  • [ 6 ] [Li, Yu]Faculty of Information Technology, Beijing University of Technology, No. 100 PingLeYuan, Chaoyang District, Beijing; 100124, China
  • [ 7 ] [Zhang, Yu]State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin; 300072, China
  • [ 8 ] [Zhang, Yu]Binhai International Advanced Structural Integrity Research Centre, Tianjin; 300072, China
  • [ 9 ] [Li, Jian]State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin; 300072, China
  • [ 10 ] [Li, Jian]Binhai International Advanced Structural Integrity Research Centre, Tianjin; 300072, China
  • [ 11 ] [Zeng, Zhoumo]State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin; 300072, China
  • [ 12 ] [Zeng, Zhoumo]Binhai International Advanced Structural Integrity Research Centre, Tianjin; 300072, China

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Source :

Remote Sensing

Year: 2022

Issue: 16

Volume: 14

5 . 0

JCR@2022

5 . 0 0 0

JCR@2022

ESI Discipline: GEOSCIENCES;

ESI HC Threshold:38

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 12

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 29

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