Stripe segmentation of oceanic internal waves in SAR images based on Gabor transform and K-means clustering

• Autorzy: Qi Kai-Tuo , Zhang Hong-Sheng , Zheng Ying-Gang , Zhang Yu , Ding Long-Yu

Identyfikatory

DOI

10.1016/j.oceano.2023.06.006

• Języki publikacji: EN

Abstrakty

EN

Oceanic internal waves are an active ocean phenomenon that can be observed, and their relevant characteristics can be acquired using synthetic aperture radar (SAR). The locations of oceanic internal waves must be determined first to obtain the important parameters of oceanic internal waves from SAR images. An oceanic internal wave segmentation method with integrated light and dark stripes was described in this study. To extract the SAR image characteristics of oceanic internal waves, the Gabor transform was initially used, and then the K-means clustering algorithm was used to separate the light (dark) stripes of oceanic internal waves from the background in the SAR images. The regions of the dark (light) stripes were automatically determined based on the differences between the three classes, that is, the dark stripes, light stripes, and background area. Finally, the locations of the dark (light) stripes were determined by shifting a given distance along the normal direction of the long side with the minimum bounding rectangle of the light (dark) stripes. The best segmentation results were obtained based on the intersection over the union of the images, and the accuracy of segmentation was verified. Furthermore, the effectiveness and practicability of the proposed method in the light and dark stripe segmentation of SAR images of oceanic internal waves were illustrated. The proposed method prepares the foundation for future inversion studies of oceanic internal waves.

Słowa kluczowe

EN

oceanic internal waves; synthetic aperture radar; Gabor transform; K-means clustering; stripe segmentation

• Wydawca: Polish Academy of Sciences, Institute of Oceanology ; Elsevier
• Czasopismo: Oceanologia
• Rocznik: 2023
• Tom: Vol. 65 (4)
• Strony: 548--555
• Opis fizyczny: Bibliogr. 20 poz., fot., tab., wykr.

Twórcy

autor

Qi Kai-Tuo

• College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China

autor

Zhang Hong-Sheng

• College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China

autor

Zheng Ying-Gang

• Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China

• https://orcid.org/0000-0001-6947-734X

autor

Zhang Yu

• College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing, China

autor

Ding Long-Yu

• College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023). (PL)