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

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Just published in @PeerJCompSci - Oil spill identification in X-band marine radar image using K-means and texture feature Read the full article https://t.co/68O7p3BVtm #OilSpill #ComputerVision #InformationSystems #NeuralNetworks
Just published in @PeerJCompSci - Oil spill identification in X-band marine radar image using K-means and texture feature Read the full article https://t.co/1Rye2NkvoF #OilSpill #ComputerVision #InformationSystems #NeuralNetworks
PeerJ Computer Science

Main article text

 

Introduction

Materials & Methods

Study area and experimental data

Study area

Experimental data

Data preprocess

Experimental method

Texture feature extraction based on the GLCM

K-means clustering algorithm

Local adaptive threshold segmentation algorithm

Result

Image slices

Texture feature extraction and selection

Image classification

Threshold segmentation

Validation

Discussion

Comparison of texture features

Comparison of local adaptive thresholds

Comparison with other methods in oil spill identification

Comparison of texture feature slice window sizes

Comparison with other machine learning classifiers

Conclusion

Supplemental Information

Oil spill shipborne radar image

DOI: 10.7717/peerj-cs.1133/supp-1

Codes of data image slice, classification, recovery and visualization

DOI: 10.7717/peerj-cs.1133/supp-2

Additional Information and Declarations

Competing Interests

The authors declare there are no competing interests.

Author Contributions

Rong Chen performed the experiments, analyzed the data, performed the computation work, authored or reviewed drafts of the article, and approved the final draft.

Bo Li performed the experiments, performed the computation work, authored or reviewed drafts of the article, and approved the final draft.

Baozhu Jia conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.

Jin Xu conceived and designed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Long Ma performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Hongbo Yang performed the computation work, prepared figures and/or tables, and approved the final draft.

Haixia Wang performed the computation work, prepared figures and/or tables, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The codes are available in the Supplementary File.

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.

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