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Abstract: 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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Key words:
- oceanic internal waves /
- deep learning /
- stripe segmentation /
- synthetic aperture radar /
- TransUNet
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Figure 2. Oceanic internal wave from synthetic aperture radar image of ERS-2 satellite.
Figure 3. Steps for collecting the ocean internal wave synthetic aperture radar data sets.
Figure 4. TransUNet framework structure, Transformer structure (a) and Cross-network structure (b). The parameters in the bracket are image size, image size and dimension, respectively. MSA: multihead self-attention; MLP: multi-layer perceptron; Conv: convolution kernel; ReLU: activation function.
Figure 7. Test set segmentation results of different multi-layer perceptron (MLP) channels. a is the original image; MLP channels are 128 (b), 256 (c), 512 (d), 768 (e), and 1024 (f).
Figure 9. Model performance analysis of the original TransUNet (a) and the optimized TransUNet (b). DSC: dice similarity coefficient.
Figure 10. Qualitative comparison of different approaches by visualization. The original image (a), different approaches by original TransUNet (b), optimized TransUNet (c) and U-Net (d).
Figure 11. Results of the entire synthetic aperture radar image segmentation (resolution size is 4903 × 5151).
Figure 12. Small-scale synthetic aperture radar image segmentation results (a), resolution sizes are 334 × 305 (b) and 282 × 348 (c). d is the segmentation result of b, e is the segmentation result of c.
Table 1. The effects of Transformer layer and multi-layer perceptron (MLP) channel on dice similarity coefficient accuracy (%)
Transformer layer 1 2 4 8 12 MLP channel 128 84.13 84.18 82.35 82.64 75.22 MLP channel 256 84.15 83.75 83.49 82.50 72.92 MLP channel 512 83.73 84.57 84.19 82.61 73.73 MLP channel 768 84.09 83.95 80.37 82.49 77.2 MLP channel 1024 84.00 84.89 80.54 82.66 76.6 
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Table 2. Model training performance at different rates
Rate Training loss Test loss 6:2:2 0.1797 0.3191 7:1.5:1.5 0.1902 0.2806 8:1:1 0.1575 0.2039 9:0.5:0.5 0.1564 0.2585
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