Deep Learning in the Mapping of Agricultural Land Use Using Sentinel-2 Satellite Data
Abstract
:1. Introduction
2. Study Area and Dataset
3. Methodology
3.1. Preprocessing of the Input Data
3.2. Deep Learning and Machine Learning
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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References | Classes | Accuracy | Satellite Dataset | Classifiers |
---|---|---|---|---|
[40] | 10 | 77% | WV-2 1 | ANN 2 |
[41] | 22 | 86.2% | Colour Plant Images | DCNN 3 |
[42] | 11 | 85% | Sentinel-1 | CNN 4 |
[25] | 11 | 96% | Sentinel-1 | RNN 5, K-NN 6, RF 7 and VSM 8 |
[43] | 5 | 87.3% | Landsat-8, Sentinel-2 | ML 9 |
[44] | 14 | 92% | Sentinel-2 | ML 9 and DL 10 |
[45] | 8 | 94.94% | Sentinel-1, Sentinel-2 | TWINNS 11 |
[46] | 3 | 97.53% | Digital images | R-CNN 12 |
[47] | 4 | 94.85% | Landsat-8, Sentinel-2 | FCNs 13 |
[48] | 15 | 96.5% | Sentinel-2 | R-CNN 12 |
[49] | 10 | 98.7% | Sentinel-2 | CNN 4 and R-CNN 12 |
[50] | 2 | 91% | Landsat-8, Sentinel-2 | DL 10 |
[27] | 5 | 88% | Sentinel-2 | DL 10 |
[17] | 21 | 77.6% | Sentinel-2 | RF 7, SVM 14 |
[15] | 8 | 90.33% | Sentinel-1, Sentinel-2 | RF 7 |
[6] | 5 | 97.2%, | Sentinel-2 | DLCD 15, RF 7, CNN 4, SVM 14 |
[8] | 6 | 96.7% | Landsat-5, Landsat-8 | CA-ANN 16, SVM 14 |
[3] | 9 | 97.22% | Sentinel-2 | KNN 17, RF 7 |
[1] | 6 | 95.48% | Landsat-8, Sentinel-2 | RF 7, SVM 14 |
Total No. of Class Categories | Parameters of Accuracy Assessment | ||||||
---|---|---|---|---|---|---|---|
RT (%) | CT (%) | NC (%) | PA (%) | UA (%) | k | ||
U-Net | Wheat | 38.6 | 39.2 | 39.4 | 98.9 | 97.4 | 0.95 |
Berseem | 12.6 | 12.4 | 12.4 | 96.8 | 98.3 | 0.98 | |
Mustard | 5 | 5.2 | 5.1 | 100 | 96.1 | 0.95 | |
Other vegetation | 33.6 | 34.2 | 34.3 | 100 | 98.2 | 0.97 | |
Water | 5 | 4.6 | 4.4 | 88 | 95.6 | 0.95 | |
Buildup | 5.2 | 4.4 | 4.4 | 84.6 | 100 | 1.00 | |
OA = 97.8%; k = 0.9691 | |||||||
RF | Wheat | 38.2 | 39.2 | 39 | 98.9 | 95.9 | 0.93 |
Berseem | 12.4 | 12.4 | 12.4 | 95.1 | 95.1 | 0.94 | |
Mustard | 5.8 | 5.2 | 5.6 | 89.6 | 100 | 1.00 | |
Other vegetation | 33.6 | 34.2 | 33.8 | 98.7 | 95.3 | 0.93 | |
Water | 5.2 | 4.6 | 4.7 | 88.4 | 100 | 1.00 | |
Buildup | 4.8 | 4.4 | 4.5 | 91.6 | 100 | 1.00 | |
OA = 96.2%; k = 0.9469 |
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Singh, G.; Singh, S.; Sethi, G.; Sood, V. Deep Learning in the Mapping of Agricultural Land Use Using Sentinel-2 Satellite Data. Geographies 2022, 2, 691-700. https://doi.org/10.3390/geographies2040042
Singh G, Singh S, Sethi G, Sood V. Deep Learning in the Mapping of Agricultural Land Use Using Sentinel-2 Satellite Data. Geographies. 2022; 2(4):691-700. https://doi.org/10.3390/geographies2040042
Chicago/Turabian StyleSingh, Gurwinder, Sartajvir Singh, Ganesh Sethi, and Vishakha Sood. 2022. "Deep Learning in the Mapping of Agricultural Land Use Using Sentinel-2 Satellite Data" Geographies 2, no. 4: 691-700. https://doi.org/10.3390/geographies2040042