The Suitability of Machine-Learning Algorithms for the Automatic Acoustic Seafloor Classification of Hard Substrate Habitats in the German Bight
Abstract
:1. Introduction
2. Geographical Setting and Study Sites
3. Material and Methods
3.1. Spatial Mapping with a SSS System
System | Frequencies | Horizontal Beam Width | Vertical Beam Width | Across-Track Resolution | File Formats |
---|---|---|---|---|---|
EdgeTech 4200 MP | 300 kHz | 0.54° | 50° | 3 cm | .jsf, .xtf |
600 kHz | 0.34° | 1.5 cm |
3.2. Ground-Truth Data from Grab Samples
3.3. Quantization Level and Size of the Image Patches for the Classification
3.4. Generating Training, Test and Validation Data from Sample Locations
3.5. Machine-Learning Algorithms
3.6. Input Data and Input Data Processing
4. Experimental Results
4.1. Impact of the Quantization Level and Image Patch Size
4.2. Impact of the Machine-Learning Algorithm and Input Data
4.3. Model Performances on Different Seafloor Sediment Classes
4.4. Visual Assessment of the Classification Maps
5. Discussion
6. Conclusions and Outlook
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Used Features | 32 px | 16 px | 8 px | ||||
---|---|---|---|---|---|---|---|
8-bit | 6-bit | 8-bit | 6-bit | 8-bit | 6-bit | ||
SVM-L and RF | All | 8 | 12 | 15 | 45 | 20 | 114 |
AGG | 1389 | 1316 | 1493 | 1453 | 1429 | 1431 | |
GLCM | 17 | 303 | 22 | 327 | 21 | 322 | |
WT | 13 | 12 | 50 | 52 | 168 | 172 | |
CNN | All | 13 | 12 | 309 | 304 | 1190 | 1272 |
Gray * | - | - | - | - | - | - | |
PWT | 13 | 12 | 309 | 304 | 1190 | 1272 |
Used Features | 32 px | 16 px | 8 px | ||||
---|---|---|---|---|---|---|---|
8-bit | 6-bit | 8-bit | 6-bit | 8-bit | 6-bit | ||
SVM-L | All | 0.49 | 0.50 | 0.44 | 0.45 | 0.36 | 0.36 |
AGG | 0.38 | 0.37 | 0.37 | 0.38 | 0.37 | 0.38 | |
GLCM | 0.52 | 0.52 | 0.46 | 0.47 | 0.39 | 0.37 | |
WT | 0.22 | 0.21 | 0.12 | 0.13 | 0.05 | 0.05 | |
RF | All | 0.57 * | 0.55 | 0.42 | 0.47 | 0.39 | 0.38 |
AGG | 0.43 | 0.43 | 0.39 | 0.42 | 0.39 | 0.39 | |
GLCM | 0.57 * | 0.57 * | 0.49 | 0.48 | 0.37 | 0.38 | |
WT | 0.27 | 0.26 | 0.12 | 0.10 | 0.05 | 0.03 | |
CNN | All | 0.41 | 0.42 | 0.38 | 0.37 | 0.33 | 0.31 |
Gray | 0.43 | 0.44 | 0.39 | 0.40 | 0.37 | 0.37 | |
PWT | 0.18 | 0.24 | 0.06 | 0.09 | 0.06 | 0.03 |
Used Features | 32 px | 16 px | 8 px | ||||
---|---|---|---|---|---|---|---|
8-bit | 6-bit | 8-bit | 6-bit | 8-bit | 6-bit | ||
SVM-L | All | 0.82 | 0.82 | 0.75 | 0.75 | 0.66 | 0.67 |
AGG | 0.70 | 0.70 | 0.69 | 0.69 | 0.62 | 0.62 | |
GLCM | 0.62 | 0.65 | 0.62 | 0.65 | 0.62 | 0.61 | |
WT | 0.55 | 0.55 | 0.41 | 0.41 | 0.27 | 0.27 | |
RF | All | 0.85 | 0.87 * | 0.80 | 0.79 | 0.62 | 0.65 |
AGG | 0.73 | 0.73 | 0.71 | 0.70 | 0.67 | 0.67 | |
GLCM | 0.75 | 0.76 | 0.67 | 0.70 | 0.65 | 0.64 | |
WT | 0.65 | 0.61 | 0.48 | 0.45 | 0.29 | 0.31 | |
CNN | All | 0.73 | 0.73 | 0.65 | 0.66 | 0.60 | 0.59 |
Gray | 0.85 | 0.85 | 0.79 | 0.79 | 0.71 | 0.70 | |
PWT | 0.42 | 0.52 | 0.26 | 0.38 | 0.17 | 0.24 |
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Breyer, G.; Bartholomä, A.; Pesch, R. The Suitability of Machine-Learning Algorithms for the Automatic Acoustic Seafloor Classification of Hard Substrate Habitats in the German Bight. Remote Sens. 2023, 15, 4113. https://doi.org/10.3390/rs15164113
Breyer G, Bartholomä A, Pesch R. The Suitability of Machine-Learning Algorithms for the Automatic Acoustic Seafloor Classification of Hard Substrate Habitats in the German Bight. Remote Sensing. 2023; 15(16):4113. https://doi.org/10.3390/rs15164113
Chicago/Turabian StyleBreyer, Gavin, Alexander Bartholomä, and Roland Pesch. 2023. "The Suitability of Machine-Learning Algorithms for the Automatic Acoustic Seafloor Classification of Hard Substrate Habitats in the German Bight" Remote Sensing 15, no. 16: 4113. https://doi.org/10.3390/rs15164113