Automatic Extraction of Internal Wave From Complex Background Using Polarimetric SAR and Convolutional Neural Network

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.16222-16235
• Main Authors: Cai, Jiehua, Hu, Wangjiang, Yan, Hengqian, Wang, Ning, Hong, Mei, Xing, Xueming, Zhang, Yongchui
• Format: Article
• Language: English
• Subjects: Accuracy; Automatic extraction; Data mining; deep learning (DL); internal wave (IW); Noise; Oceans; polarimetric synthetic aperture radar (SAR); Radar polarimetry; Sentinel-1; Synthetic aperture radar
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2024.3445604

As an important ocean dynamic process, internal waves (IWs) have a remarkable effect on sediment resuspension and ocean mixing, and also seriously threaten oil rig operation and the navigability of underwater submarines. Synthetic aperture radar (SAR) is an advanced tool widely applied for IW observation due to its all-weather observation capability. However, complex atmospheric and oceanic environments can obstruct the IW structures in SAR single-channel images, and accurate automated extraction still limits the application of SAR-based IW detection from complex oceanic backgrounds. This study proposes a novel automatic extraction method for IWs to address this problem. The main innovations for accurate IW extraction with SAR data are highlighted as follows. 1) Polarimetric SAR images, which contain more information to characterize IWs, are utilized. 2) An attention-based U-Net is proposed to achieve better feature-focusing capabilities of the IW extraction network. 3) A multiscale loss function is proposed to improve the accuracy of the IW extraction network. We create an IW dataset that contains 1008 scenes of Sentinel-1 images and 10 686 IW crests in the Indo-Pacific warm pool. Experimentally, the proposed method achieves good completeness, correctness, and F1 scores of 94.54%, 81.43%, and 87.50%, respectively, which are significantly higher than other methods. Further experiments indicated that the trained IW extraction network shows satisfactory applicability and accuracy for different test areas and various SAR data. The proposed method has great potential applications for performing ocean/global scale monitoring and statistical analysis of IWs.