Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery
Abstract
:1. Introduction
2. Related Work
2.1. Land Cover and Crop Classification
2.2. Domain Adaptation
3. Study Area and Data
4. Methodology
4.1. Domain-Adversarial Neural Networks
4.2. Classification of Multi-Spectral Time Series Data with Self-Attention
4.3. DANN for Land Cover and Crop Classification
5. Experiments and Discussion
5.1. Experimental Settings
5.2. Maximum Mean Discrepancy
5.3. Results Discussion and Applicability Study
- case 1: zone 2 (source), zone 3 (target). In this case DANN shows the greatest improvements with an initial high value of MMD. Features are visually reported in Figure 7: in (a,b) when extracted by standard Transformer encoder trained on the source domain, in (c,d) when extracted by DANN. The difference is visually clear. Features distributions are matched by DANN, with a resulting overlapping shape between source and target domain.
- case 2: zone 1 (source), zone 2 (target). In this case DANN shows the worst improvements with an initial low value of MMD. Features are visually reported in (a,b) of Figure 8 when extracted by standard Transformer encoder, in (c,d) of the same Figure 8 when extracted by DANN. They appear already similar also without DANN.
- case 3: zone 4 (source), zone 3 (target). In this case DANN shows noticeable improvements, regardless an initial low value of MMD. Features are visually reported in (a,b) of Figure 9 when extracted by standard Transformer encoder, in (c,d) of Figure 9 when extracted by DANN. As with case 1, the difference is visually clear, and the effect of DANN can be easily appreciated.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Barley | Wheat | Rapeseed | Corn | Sunflower | Orchards | Nuts | Permanent Meadows | Temporary Meadows | |
---|---|---|---|---|---|---|---|---|---|
Zone 1 | 13,051 | 30,380 | 5596 | 44,003 | 1 | 937 | 10 | 32,641 | 52,013 |
Zone 2 | 10,736 | 15,026 | 2349 | 36,620 | 6 | 348 | 18 | 36,536 | 39,143 |
Zone 3 | 7154 | 27,202 | 3557 | 42,011 | 10 | 1217 | 10 | 32,524 | 52,682 |
Zone 4 | 5981 | 17,009 | 3244 | 31,361 | 2 | 552 | 11 | 26,134 | 38,141 |
Zone | Transformer Encoder | DANN | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Source Domain | Target Domain | Train Accuracy | Test Accuracy | F1-Accuracy | K-Score | MMD | Test Accuracy | F1-Accuracy | K-Score | MMD |
1 | 2 | 0.8577 | 0.7877 | 0.5675 | 0.7229 | 0.1109 | 0.7628 | 0.5540 | 0.6950 | 0.0077 |
1 | 3 | 0.8577 | 0.7436 | 0.5266 | 0.6606 | 0.1620 | 0.7449 | 0.5080 | 0.6714 | 0.0183 |
1 | 4 | 0.8577 | 0.7941 | 0.5675 | 0.7294 | 0.0516 | 0.7960 | 0.5734 | 0.7343 | 0.0086 |
2 | 1 | 0.8951 | 0.7433 | 0.5309 | 0.6773 | 0.1577 | 0.7403 | 0.5161 | 0.6687 | 0.0208 |
2 | 3 | 0.8951 | 0.4967 | 0.3592 | 0.3642 | 0.6700 | 0.6505 | 0.4544 | 0.5483 | 0.0104 |
2 | 4 | 0.8951 | 0.6006 | 0.4395 | 0.4912 | 0.2536 | 0.7482 | 0.4832 | 0.6735 | 0.0416 |
3 | 1 | 0.8750 | 0.7767 | 0.5339 | 0.7122 | 0.1819 | 0.8045 | 0.5778 | 0.7488 | 0.0121 |
3 | 2 | 0.8750 | 0.6638 | 0.4594 | 0.5615 | 0.6254 | 0.7589 | 0.5334 | 0.6865 | 0.0277 |
3 | 4 | 0.8750 | 0.7348 | 0.5074 | 0.6504 | 0.1184 | 0.7968 | 0.5778 | 0.7338 | 0.0115 |
4 | 1 | 0.8870 | 0.7927 | 0.5551 | 0.7354 | 0.0339 | 0.8233 | 0.5822 | 0.7753 | 0.0039 |
4 | 2 | 0.8870 | 0.7600 | 0.5443 | 0.6870 | 0.0953 | 0.8003 | 0.5788 | 0.7399 | 0.0084 |
4 | 3 | 0.8870 | 0.7111 | 0.4961 | 0.6230 | 0.0960 | 0.7673 | 0.5443 | 0.6965 | 0.0062 |
Zone | Improvement [%] | |||
---|---|---|---|---|
Source Domain | Target Domain | Test Accuracy | F1-Accuracy | K-Score |
1 | 2 | −3.1576 | −2.3859 | −3.8508 |
1 | 3 | 0.1762 | −3.5378 | 1.6395 |
1 | 4 | 0.2296 | 1.0467 | 0.6773 |
2 | 1 | −0.3996 | −2.7935 | −1.2698 |
2 | 3 | 30.9721 | 26.4916 | 50.5414 |
2 | 4 | 24.5690 | 9.9474 | 37.1046 |
3 | 1 | 3.5803 | 8.2152 | 5.1446 |
3 | 2 | 14.3204 | 16.1075 | 22.2539 |
3 | 4 | 8.4475 | 13.8791 | 12.8283 |
4 | 1 | 3.8705 | 4.8817 | 5.4228 |
4 | 2 | 5.3053 | 6.3384 | 7.6922 |
4 | 3 | 7.9018 | 9.7154 | 11.8067 |
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Martini, M.; Mazzia, V.; Khaliq, A.; Chiaberge, M. Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery. Remote Sens. 2021, 13, 2564. https://doi.org/10.3390/rs13132564
Martini M, Mazzia V, Khaliq A, Chiaberge M. Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery. Remote Sensing. 2021; 13(13):2564. https://doi.org/10.3390/rs13132564
Chicago/Turabian StyleMartini, Mauro, Vittorio Mazzia, Aleem Khaliq, and Marcello Chiaberge. 2021. "Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery" Remote Sensing 13, no. 13: 2564. https://doi.org/10.3390/rs13132564
APA StyleMartini, M., Mazzia, V., Khaliq, A., & Chiaberge, M. (2021). Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery. Remote Sensing, 13(13), 2564. https://doi.org/10.3390/rs13132564