Optimizing the Scale of Observation for Intertidal Habitat Classification through Multiscale Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site and UAS Survey
2.2. Imagery Processing
2.3. GEOBIA—Segmentation Optimization on Representative Subset Area
2.4. GEOBIA-Segmentation and Classification of the Entire Scene
2.5. Accuracy Assessment
2.6. Multiscale Analysis
3. Results
3.1. Imagery Processing
3.2. Segmentation Optimization
3.3. Segmentation and Classification
3.4. Accuracy Assessment
3.5. Variable Importance
3.6. Mode of Classifications
3.7. Multiscale Classification
4. Discussion
4.1. Classification Performance
4.2. Considerations, Challenges, and Future Directions
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Parameter | Value Used |
---|---|
Training threshold | 0.75 |
Balance method | “ubOver” (over-sampling) |
Evaluation metric | “Kappa” (kappa coefficient) |
Evaluation method | “10FCV” (10-fold cross-validation) |
Min train cases | 20 |
Min cases by class train | 5 |
Min cases by class test | 5 |
Population size | 20 |
Probability of crossover | 0.8 |
Probability of mutation | 0.2 |
Max number of iterations | 5 |
Run | 20 |
Keep best | TRUE |
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Resolution | Spectral Radius | Spatial Radius | Min Size (Pixels) | Min Size (m2) |
---|---|---|---|---|
3 cm | 56 | 56 | 8286 | 7.46 |
5 cm | 63 | 61 | 2932 | 7.33 |
7 cm | 65 | 71 | 1626 | 7.97 |
9 cm | 71 | 39 | 931 | 7.54 |
11 cm | 62 | 70 | 740 | 8.96 |
13 cm | 69 | 55 | 455 | 7.69 |
15 cm | 63 | 45 | 329 | 7.40 |
17 cm | 67 | 55 | 261 | 7.54 |
19 cm | 86 | 59 | 207 | 7.47 |
21 cm | 76 | 77 | 161 | 7.10 |
23 cm | 86 | 73 | 133 | 7.04 |
25 cm | 74 | 39 | 113 | 7.06 |
27 cm | 66 | 60 | 107 | 7.80 |
29 cm | 89 | 72 | 92 | 7.73 |
31 cm | 71 | 45 | 77 | 7.40 |
Marsh | Mud | Oyster | Water | |||||||
---|---|---|---|---|---|---|---|---|---|---|
PA | UA | PA | UA | PA | UA | PA | UA | Overall | Kappa | |
3 cm | 92% | 90% | 49% | 86% | 87% | 74% | 96% | 75% | 80% | 0.736 |
5 cm | 92% | 86% | 73% | 77% | 78% | 80% | 88% | 86% | 82% | 0.764 |
7 cm | 88% | 89% | 73% | 71% | 72% | 84% | 87% | 76% | 79% | 0.727 |
9 cm | 91% | 92% | 69% | 83% | 75% | 77% | 90% | 74% | 81% | 0.743 |
11 cm | 93% | 92% | 80% | 74% | 75% | 78% | 77% | 83% | 81% | 0.749 |
13 cm | 89% | 93% | 72% | 74% | 74% | 76% | 83% | 77% | 79% | 0.723 |
15 cm | 88% | 95% | 74% | 74% | 81% | 71% | 77% | 82% | 80% | 0.728 |
17 cm | 91% | 89% | 78% | 75% | 73% | 84% | 86% | 80% | 82% | 0.757 |
19 cm | 85% | 95% | 56% | 80% | 80% | 76% | 93% | 69% | 78% | 0.713 |
21 cm | 93% | 91% | 72% | 78% | 78% | 82% | 87% | 78% | 82% | 0.759 |
23 cm | 88% | 96% | 67% | 74% | 78% | 81% | 87% | 72% | 80% | 0.732 |
25 cm | 88% | 90% | 72% | 78% | 78% | 81% | 89% | 77% | 81% | 0.751 |
27 cm | 95% | 89% | 67% | 83% | 82% | 71% | 82% | 83% | 81% | 0.745 |
29 cm | 85% | 90% | 60% | 82% | 73% | 84% | 97% | 64% | 78% | 0.708 |
31 cm | 87% | 91% | 75% | 79% | 79% | 83% | 89% | 77% | 82% | 0.763 |
Mode | 95% | 93% | 76% | 87% | 86% | 84% | 91% | 83% | 87% | 0.821 |
Multiscale | 92% | 89% | 58% | 88% | 86% | 86% | 96% | 75% | 83% | 0.778 |
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Espriella, M.C.; Lecours, V. Optimizing the Scale of Observation for Intertidal Habitat Classification through Multiscale Analysis. Drones 2022, 6, 140. https://doi.org/10.3390/drones6060140
Espriella MC, Lecours V. Optimizing the Scale of Observation for Intertidal Habitat Classification through Multiscale Analysis. Drones. 2022; 6(6):140. https://doi.org/10.3390/drones6060140
Chicago/Turabian StyleEspriella, Michael C., and Vincent Lecours. 2022. "Optimizing the Scale of Observation for Intertidal Habitat Classification through Multiscale Analysis" Drones 6, no. 6: 140. https://doi.org/10.3390/drones6060140