Volume 42 Issue 10
Oct.  2023
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Kaituo Qi, Hongsheng Zhang, Jiaojiao Lu, Yinggang Zheng, Zhouhao Zhang. Strip segmentation of oceanic internal waves in SAR images based on TransUNet[J]. Acta Oceanologica Sinica, 2023, 42(10): 67-74. doi: 10.1007/s13131-023-2206-6
Citation: Kaituo Qi, Hongsheng Zhang, Jiaojiao Lu, Yinggang Zheng, Zhouhao Zhang. Strip segmentation of oceanic internal waves in SAR images based on TransUNet[J]. Acta Oceanologica Sinica, 2023, 42(10): 67-74. doi: 10.1007/s13131-023-2206-6

Strip segmentation of oceanic internal waves in SAR images based on TransUNet

doi: 10.1007/s13131-023-2206-6
Funds:  The National Natural Science Foundation of China under contract No. 51679132; the Science and Technology Commission of Shanghai Municipality under contract Nos. 21ZR1427000 and 17040501600.
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  • Corresponding author: E-mail: hszhang@shmtu.edu.cningopro@126.com
  • Received Date: 2023-01-15
  • Accepted Date: 2023-04-13
  • Available Online: 2023-07-18
  • Publish Date: 2023-10-01
  • The development of oceanic remote sensing artificial intelligence has made possible to obtain valuable information from amounts of massive data. Oceanic internal waves play a crucial role in oceanic activity. To obtain oceanic internal wave stripes from synthetic aperture radar (SAR) images, a stripe segmentation algorithm is proposed based on the TransUNet framework, which is a combination of U-Net and Transformer, which is also optimized. Through adjusting the number of Transformer layer, multi-layer perceptron (MLP) channel, and Dropout parameters, the influence of over-fitting on accuracy is significantly weakened, which is more conducive to segmenting lightweight oceanic internal waves. The results show that the optimized algorithm can accurately segment oceanic internal wave stripes. Moreover, the optimized algorithm can be trained on a microcomputer, thus reducing the research threshold. The proposed algorithm can also change the complexity of the model to adapt it to different date scales. Therefore, TransUNet has immense potential for segmenting oceanic internal waves.
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