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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, 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 SAR image of ERS-2 satellite (4903×5151 SAR_IMS_1PNESA19980316_035107_00000018A030_00290_15174_0000).
Figure 4. TransUNet framework structure, Transformer structure (a) and Cross-network structure (b).
Figure 7. Test set segmentation results of different MLP channels. The original image (a), 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).
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 12. Small-scale SAR image segmentation results, (b) Resolution size is 334 × 305 (c) resolution size is 282 × 348.
Figure 13. Results of the entire SAR image segmentation（9501×7915ALOS-ALPSRP 055520130-L1.1）.
Table 1. The effects of Transformer layer and MLP channel on DSC accuracy (%)
Transformer layer 1 2 4 8 12 MLP channel 128 84.13 84.18 82.35 82.64 75.22 MLP channel 258 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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