Acta Photonica Sinica, Volume. 51, Issue 4, 0410003(2022)
SAR Image Denoising and Semantic Enhancement for Object Detection
Figures & Tables(19)
Table 1. Data set parameters
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Datasets Sensors Resolution/m Polarization Scenes SSDD RadarSat-2
TerraSAR-X
Sentinel-1
1~5 HH，HV，VV，VH Offshore，Inshore，
Coherent speckle noise
SAR-Ship-Dataset ［ 17 ］Sentinel-1
GF-3
3，5，8，10，25 HH，HV，VV，VH HRSID ［ 18 ］Sentinel-1B
TerraSAR-X
TanDEM
0.5，1，3 HH，HV，VV，VH
Table 2. Detection performance of different backbone networks
View tableView in ArticleTable 2. Detection performance of different backbone networks
Backbone Recall/% Precision/% Average precision/% Resnet50 97.07 95.13 96.53 Resnet101 97.58 96.11 96.73
Table 3. Detection performance of different models
View tableView in ArticleTable 3. Detection performance of different models
Pixel-level noise reduction Semantic enhancement Recall/% Precision/% Average precision/% × × 89.94 94.26 89.23 √ × 96.94 96.33 96.44 × √ 96.93 95.84 96.40 √ √ 97.58 96.11 96.73
Table 4. Model performance analysis
View tableView in ArticleTable 4. Model performance analysis
Pixel-level noise reduction Semantic enhancement Params/M Training time /FPS Testing time /FPS × × 152.76 6.89 22.64 √ × 160.84 6.49 20.50 × √ 151.40 6.10 20.92 √ √ 159.49 5.99 21.09
Table 5. Detection performance of different methods on SSDD
View tableView in ArticleTable 5. Detection performance of different methods on SSDD
Methods Backbone Recall/% Precision/% Average precision/% Faster R-CNN Resnet101 88.54 88.09 87.26 Retinanet Resnet101 89.17 87.94 86.91 FPN Resnet101 92.22 86.41 90.69 YOLOv4 CSPDarknet53 - - 95.60 WANG［ 24 ］ShuffleNetV2 - - 94.70 R2FA-Det［ 25 ］Resnet50 - - 94.72 FBR-Net ［ 26 ］- 92.79 94.01 94.10 Ours Resnet101 97.58 96.11 96.73
Table 6. Detection performance of different methods on SAR-Ship-Dataset
View tableView in ArticleTable 6. Detection performance of different methods on SAR-Ship-Dataset
Methods Backbone Recall/% Precision/% Average precision /% Faster R-CNN Resnet101 80.24 81.39 76.35 Retinanet Resnet101 72.17 90.62 71.03 FPN Resnet101 83.40 90.80 81.38 Ours Resnet101 91.86 81.45 88.86
Table 7. Detection performance of different scenes
View tableView in ArticleTable 7. Detection performance of different scenes
Scenes Methods Recall/% Precision/% Average precision/% Inshore Baseline 79.01 87.07 77.79 Our 91.98 89.76 90.00 Offshore Baseline 92.78 96.01 92.24 Our 99.04 97.78 98.53 Small-scale target Baseline 90.79 90.78 90.66 Our 97.60 96.78 96.85 Large-scale target Baseline 89.84 94.65 89.13 Our 97.37 92.50 94.64
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Haicheng QU, Lei SHEN. SAR Image Denoising and Semantic Enhancement for Object Detection[J]. Acta Photonica Sinica, 2022, 51(4): 0410003
Paper Information
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Received: Oct. 27, 2021
Accepted: Dec. 30, 2021
Published Online: May. 18, 2022
The Author Email: SHEN Lei (shenlei95821@163.com)
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