Oil spill identification in X-band marine radar image using K-means and texture feature

Rong Chen; Bo Li; Baozhu Jia; Jin Xu; Long Ma; Hongbo Yang; Haixia Wang

doi:10.7717/peerj-cs.1133

Oil spill identification in X-band marine radar image using K-means and texture feature

PeerJ Comput Sci. 2022 Oct 24:8:e1133. doi: 10.7717/peerj-cs.1133. eCollection 2022.

Authors

Rong Chen^#¹, Bo Li^#¹, Baozhu Jia^{1

2}, Jin Xu¹, Long Ma¹, Hongbo Yang¹, Haixia Wang³

Affiliations

¹ Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, Guangdong, China.
² Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province, Guangdong, China.
³ Navigation College, Dalian Martime University, Dalian, Liaoning, China.

^# Contributed equally.

Abstract

Marine oil pollution poses a serious threat to the marine ecological balance. It is of great significance to develop rapid and efficient oil spill detection methods for the mitigation of marine oil spill pollution and the restoration of the marine ecological environment. X-band marine radar is one of the important monitoring devices, in this article, we perform the digital X-band radar image by "Sperry Marine" radar system for an oil film extraction experiment. First, the de-noised image was obtained by preprocessing the original image in the Cartesian coordinate system. Second, it was cut into slices. Third, the texture features of the slices were calculated based on the gray-level co-occurrence matrix (GLCM) and K-means method to extract the rough oil spill regions. Finally, the oil spill regions were segmented using the Sauvola threshold algorithm. The experimental results indicate that this study provides a scientific method for the research of oil film extraction. Compared with other methods of oil spill extraction in X-band single-polarization marine radar images, the proposed technology is more intelligent, and it can provide technical support for marine oil spill emergency response in the future.

Keywords: GLCM; K-means; Local adaptive threshold; Oil spill extraction; Texture feature.

Grants and funding

This work was supported by the National Natural Science Foundation of China (No. 52071090), the Natural Science Foundation of Guangdong Province (2022A1515011603), the Research start-up funding project of Guangdong Ocean University (060302132009), the Universitiy Special projects of Guangdong Province (2020ZDX3063). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.