A Texture Feature Removal Network for Sonar Image Classification and Detection
Abstract
1. Introduction
2. Materials and Methods
2.1. Problem Definition
2.2. Texture Feature Removal Network
3. Results
3.1. Supervised Transfer Learning Experiments of the Side-Scan Sonar Image Classification Task
3.1.1. Source Domain Dataset
3.1.2. Side-Scan Sonar Image Dataset
3.1.3. Supervised Transfer Learning Experiments
3.2. Unsupervised Transfer Learning Experiment on the Side-Scan Sonar Image Classification Task
3.3. Imaging Sonar Object Detection Experiments and Results
3.3.1. Dataset
3.3.2. Forward-Looking Sonar Image Detection Experiments
4. Discussion
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Airplane 13 Samples | Shipwreck 71 Samples | Other 106 Samples | Global Accuracy | Mean Accuracy | |
---|---|---|---|---|---|
6 | 45 | 100 | 0.7947 | 0.6796 | |
9 | 67 | 100 | 0.9316 | 0.8645 | |
Our method | 11 | 66 | 101 | 0.9368 | 0.9095 |
Airplane 33 Samples | Shipwreck 179 Samples | Other 265 Samples | Global Accuracy | Mean Accuracy | |
---|---|---|---|---|---|
DaNN | 19 | 103 | 251 | 0.782 | 0.6995 |
Our method | 22 | 122 | 244 | 0.8134 | 0.7563 |
Missing Detections | Incorrect Detections | |||
---|---|---|---|---|
0.039 | 0.231 | 9 | 1 | |
0.246 | 0.632 | 2 | 1 | |
Our method | 0.356 | 0.711 | 0 | 0 |
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Li, C.; Ye, X.; Xi, J.; Jia, Y. A Texture Feature Removal Network for Sonar Image Classification and Detection. Remote Sens. 2023, 15, 616. https://doi.org/10.3390/rs15030616
Li C, Ye X, Xi J, Jia Y. A Texture Feature Removal Network for Sonar Image Classification and Detection. Remote Sensing. 2023; 15(3):616. https://doi.org/10.3390/rs15030616
Chicago/Turabian StyleLi, Chuanlong, Xiufen Ye, Jier Xi, and Yunpeng Jia. 2023. "A Texture Feature Removal Network for Sonar Image Classification and Detection" Remote Sensing 15, no. 3: 616. https://doi.org/10.3390/rs15030616
APA StyleLi, C., Ye, X., Xi, J., & Jia, Y. (2023). A Texture Feature Removal Network for Sonar Image Classification and Detection. Remote Sensing, 15(3), 616. https://doi.org/10.3390/rs15030616