Long-Term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Areas
2.2. Data Preprocessing and Collection
2.2.1. Preprocessing
2.2.2. Training Data Collection
2.2.3. Validation Data Collection
2.3. Method
2.3.1. Offline Training
2.3.2. Online Optimization
3. Results
3.1. Temporal Transfer
3.1.1. Performance of the Initial Classification
3.1.2. Urban Expansion
3.2. Spatial Transfer
3.2.1. Performance of the Initial Classification
3.2.2. Urban Expansion
3.3. Comparisons with State-of-the-Art Methods
4. Discussion
4.1. Applicability of Spectral Information from Landsat
4.2. Variations in Urban Expansion Patterns among Cities
4.3. Limitations of Transfer Learning for Urban Mapping
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Year—DOY | FN | TN | FP | TP | OA | UA-N | UA-U | ||
---|---|---|---|---|---|---|---|---|---|
Beijing | 1984 | 1984229 | 314 | 9686 | 201 | 9799 | 0.97 | 0.97 | 0.98 |
1984277 | 258 | 9742 | 318 | 9682 | 0.97 | 0.97 | 0.97 | ||
Merged | 198 | 9802 | 54 | 9956 | 0.99 | 0.98 | 0.99 | ||
1994 | 1994160 | 1888 | 8112 | 36 | 9964 | 0.90 | 0.81 | 1.00 | |
1994256 | 164 | 9836 | 97 | 9903 | 0.99 | 0.98 | 0.99 | ||
1994336 | 1438 | 8562 | 569 | 9431 | 0.90 | 0.86 | 0.94 | ||
Merged | 118 | 9882 | 71 | 9929 | 0.99 | 0.99 | 0.99 | ||
2004 | 2004004 | 1024 | 8976 | 14 | 9986 | 0.95 | 0.90 | 1.00 | |
2004028 | 249 | 9483 | 166 | 9409 | 0.98 | 0.97 | 0.98 | ||
2004036 | 215 | 7158 | 161 | 8626 | 0.98 | 0.97 | 0.98 | ||
2004068 | 447 | 7902 | 135 | 8402 | 0.97 | 0.95 | 0.98 | ||
2004092 | 197 | 9506 | 173 | 9803 | 0.98 | 0.98 | 0.98 | ||
2004100 | 620 | 7968 | 78 | 8257 | 0.96 | 0.93 | 0.99 | ||
2004108 | 723 | 9511 | 123 | 9128 | 0.96 | 0.93 | 0.99 | ||
2004196 | 870 | 7260 | 25 | 7963 | 0.94 | 0.89 | 1.00 | ||
2004244 | 359 | 7469 | 19 | 8679 | 0.98 | 0.95 | 1.00 | ||
2004252 | 131 | 9640 | 68 | 9869 | 0.99 | 0.99 | 0.99 | ||
2004292 | 1608 | 8825 | 196 | 6158 | 0.89 | 0.85 | 0.97 | ||
2004300 | 244 | 9645 | 99 | 9754 | 0.98 | 0.98 | 0.99 | ||
2004308 | 527 | 7073 | 25 | 4981 | 0.96 | 0.93 | 1.00 | ||
2004324 | 619 | 7511 | 127 | 8363 | 0.96 | 0.92 | 0.99 | ||
2004332 | 509 | 8889 | 125 | 9078 | 0.97 | 0.95 | 0.99 | ||
2004340 | 322 | 7528 | 204 | 8484 | 0.97 | 0.96 | 0.98 | ||
Merged | 110 | 9890 | 17 | 9983 | 0.99 | 0.99 | 1.00 | ||
2014 | 2014015 | 1235 | 6533 | 1383 | 7362 | 0.84 | 0.84 | 0.84 | |
2014063 | 327 | 8754 | 30 | 7155 | 0.98 | 0.96 | 1.00 | ||
2014079 | 872 | 8228 | 735 | 7671 | 0.91 | 0.90 | 0.91 | ||
2014095 | 274 | 8347 | 532 | 8934 | 0.96 | 0.97 | 0.94 | ||
2014119 | 1099 | 7901 | 253 | 9747 | 0.93 | 0.88 | 0.97 | ||
2014127 | 1469 | 7571 | 40 | 7765 | 0.91 | 0.84 | 0.99 | ||
2014135 | 944 | 9036 | 326 | 9092 | 0.93 | 0.91 | 0.97 | ||
2014191 | 276 | 6834 | 15 | 7946 | 0.98 | 0.96 | 1.00 | ||
2014207 | 1053 | 8135 | 139 | 7551 | 0.93 | 0.89 | 0.98 | ||
2014231 | 493 | 9507 | 139 | 9615 | 0.97 | 0.95 | 0.99 | ||
2014239 | 616 | 8400 | 72 | 6398 | 0.96 | 0.93 | 0.99 | ||
2014247 | 985 | 9015 | 49 | 9951 | 0.95 | 0.90 | 1.00 | ||
2014279 | 945 | 9055 | 659 | 9341 | 0.92 | 0.91 | 0.93 | ||
2014287 | 356 | 8688 | 78 | 7728 | 0.97 | 0.96 | 0.99 | ||
2014335 | 693 | 8520 | 147 | 8402 | 0.95 | 0.92 | 0.98 | ||
2014351 | 1647 | 7080 | 65 | 7690 | 0.90 | 0.81 | 0.99 | ||
2014359 | 1510 | 7835 | 65 | 9925 | 0.92 | 0.84 | 0.99 | ||
Merged | 71 | 9929 | 28 | 9972 | 1.00 | 0.99 | 1.00 |
Year—DOY | FN | TN | FP | TP | OA | UA-N | UA-U | ||
---|---|---|---|---|---|---|---|---|---|
New York | 1986 | 1986087 | 529 | 8938 | 131 | 9869 | 0.97 | 0.94 | 0.99 |
1986151 | 148 | 9777 | 21 | 9752 | 0.99 | 0.99 | 1.00 | ||
1986295 | 435 | 9565 | 126 | 9080 | 0.97 | 0.96 | 0.99 | ||
Merged | 174 | 9826 | 12 | 9988 | 0.99 | 0.98 | 1.00 | ||
1997 | 1997149 | 98 | 5863 | 156 | 7100 | 0.98 | 0.98 | 0.98 | |
1997229 | 21 | 9979 | 28 | 7912 | 1.00 | 1.00 | 1.00 | ||
1997293 | 122 | 9736 | 69 | 8912 | 0.99 | 0.99 | 0.99 | ||
Merged | 17 | 9983 | 46 | 9954 | 1.00 | 1.00 | 1.00 | ||
2004 | 2004001 | 110 | 7870 | 85 | 5123 | 0.99 | 0.99 | 0.98 | |
2004017 | 85 | 5123 | 2 | 678 | 0.99 | 0.98 | 1.00 | ||
2004033 | 134 | 9866 | 40 | 9960 | 0.99 | 0.99 | 1.00 | ||
2004073 | 416 | 9584 | 400 | 9600 | 0.96 | 0.96 | 0.96 | ||
2004097 | 637 | 9363 | 266 | 9734 | 0.95 | 0.94 | 0.97 | ||
2004161 | 3700 | 6230 | 240 | 9776 | 0.80 | 0.63 | 0.98 | ||
2004185 | 544 | 9456 | 53 | 9947 | 0.97 | 0.95 | 0.99 | ||
2004193 | 303 | 9697 | 27 | 9973 | 0.98 | 0.97 | 1.00 | ||
2004233 | 386 | 9614 | 190 | 9910 | 0.97 | 0.96 | 0.98 | ||
2004257 | 335 | 6461 | 39 | 6830 | 0.97 | 0.95 | 0.99 | ||
2004281 | 167 | 9129 | 40 | 9960 | 0.99 | 0.98 | 1.00 | ||
Merged | 21 | 9979 | 8 | 9992 | 1.00 | 1.00 | 1.00 | ||
2014 | 2014100 | 340 | 9629 | 86 | 8850 | 0.98 | 0.97 | 0.99 | |
2014140 | 229 | 6805 | 14 | 5230 | 0.98 | 0.97 | 1.00 | ||
2014180 | 120 | 7653 | 57 | 9867 | 0.99 | 0.98 | 0.99 | ||
2014204 | 78 | 7021 | 135 | 6876 | 0.98 | 0.99 | 0.98 | ||
2014212 | 245 | 9021 | 157 | 8484 | 0.98 | 0.97 | 0.98 | ||
2014220 | 113 | 6792 | 151 | 5595 | 0.98 | 0.98 | 0.97 | ||
2014236 | 115 | 4811 | 197 | 5824 | 0.97 | 0.98 | 0.97 | ||
2014260 | 238 | 8913 | 63 | 8751 | 0.98 | 0.97 | 0.99 | ||
2014300 | 356 | 6393 | 51 | 3677 | 0.96 | 0.95 | 0.99 | ||
Merged | 170 | 9830 | 11 | 9927 | 0.99 | 0.98 | 1.00 | ||
Melbourne | 1986 | 1986231(Merged) | 573 | 9427 | 65 | 9935 | 0.97 | 0.94 | 0.99 |
1997 | 1997069 | 691 | 5689 | 57 | 9387 | 0.95 | 0.89 | 0.99 | |
1997085 | 59 | 9723 | 20 | 8576 | 1.00 | 0.99 | 1.00 | ||
1997101 | 139 | 8971 | 38 | 2528 | 0.98 | 0.98 | 0.99 | ||
1997149 | 439 | 8770 | 33 | 7739 | 0.97 | 0.95 | 1.00 | ||
1997309 | 340 | 9439 | 133 | 9524 | 0.98 | 0.97 | 0.99 | ||
1997325 | 371 | 9626 | 124 | 9871 | 0.98 | 0.96 | 0.99 | ||
Merged | 312 | 9688 | 10 | 9990 | 0.98 | 0.97 | 1.00 | ||
2004 | 2004001 | 224 | 6899 | 193 | 7837 | 0.97 | 0.97 | 0.98 | |
2004097 | 266 | 5686 | 542 | 6048 | 0.94 | 0.96 | 0.92 | ||
2004185 | 447 | 8054 | 68 | 7166 | 0.97 | 0.95 | 0.99 | ||
2004249 | 88 | 8981 | 12 | 9173 | 0.99 | 0.99 | 1.00 | ||
2004297 | 61 | 8152 | 31 | 9688 | 0.99 | 0.99 | 1.00 | ||
2004321 | 182 | 8486 | 21 | 7365 | 0.99 | 0.98 | 1.00 | ||
2004329 | 432 | 9304 | 116 | 9880 | 0.97 | 0.96 | 0.99 | ||
Merged | 49 | 9951 | 15 | 9985 | 1.00 | 1.00 | 1.00 | ||
2014 | 2014013 | 380 | 6710 | 19 | 8410 | 0.97 | 0.95 | 1.00 | |
2014037 | 1560 | 6050 | 2 | 7970 | 0.90 | 0.80 | 1.00 | ||
2014277 | 687 | 7718 | 45 | 8526 | 0.96 | 0.92 | 0.99 | ||
2014285 | 322 | 7597 | 74 | 8395 | 0.98 | 0.96 | 0.99 | ||
2014293 | 929 | 9071 | 23 | 9977 | 0.95 | 0.91 | 1.00 | ||
2014333 | 286 | 8972 | 108 | 8973 | 0.98 | 0.97 | 0.99 | ||
Merged | 148 | 9852 | 15 | 9985 | 0.99 | 0.99 | 1.00 | ||
Munich | 1986 | 1986196 | 1488 | 8510 | 150 | 9817 | 0.92 | 0.85 | 0.98 |
1986212 | 558 | 7973 | 19 | 5315 | 0.96 | 0.93 | 1.00 | ||
Merged | 354 | 9646 | 74 | 9926 | 0.98 | 0.96 | 0.99 | ||
1997 | 1997258(Merged) | 274 | 9726 | 186 | 9814 | 0.98 | 0.97 | 0.98 | |
2004 | 2004214 | 795 | 7312 | 6 | 4914 | 0.94 | 0.90 | 1.00 | |
2004222 | 1691 | 7403 | 182 | 8128 | 0.89 | 0.81 | 0.98 | ||
2004246 | 205 | 9787 | 35 | 9916 | 0.99 | 0.98 | 1.00 | ||
2004254 | 607 | 7126 | 104 | 8467 | 0.96 | 0.92 | 0.99 | ||
2004262 | 873 | 9118 | 70 | 9842 | 0.95 | 0.91 | 0.99 | ||
Merged | 241 | 9759 | 14 | 9986 | 0.99 | 0.98 | 1.00 | ||
2014 | 2014073 | 285 | 7890 | 105 | 8028 | 0.98 | 0.97 | 0.99 | |
2014089 | 2102 | 7271 | 317 | 8328 | 0.87 | 0.78 | 0.96 | ||
2014097 | 451 | 8722 | 287 | 5886 | 0.95 | 0.95 | 0.95 | ||
2014113 | 1120 | 8122 | 87 | 6344 | 0.92 | 0.88 | 0.99 | ||
2014161 | 614 | 8090 | 133 | 9513 | 0.96 | 0.93 | 0.99 | ||
Merged | 282 | 9718 | 96 | 9904 | 0.98 | 0.97 | 0.99 |
SVM-RBF (%) | RF (%) | RNN-LSTM (%) | Proposed Framework (%) | ||
---|---|---|---|---|---|
Temporal transfer | Beijing | 68.63 | 71.38 | 76.25 | 81.87 |
Spatial transfer | New York | 69.13 | 72.75 | 80.63 | 82.08 |
Melbourne | 71.25 | 67.63 | 85.88 | 84.75 | |
Munich | 79.25 | 78.2 | 86.87 | 90.63 | |
Run-Time (min) | - | 7.53 | 0.37 | 0.78 | 0.82 |
Intra-Class Distance | Inter-Class Distance | |||
---|---|---|---|---|
Non-Urban | Urban | Non-Urban To Urban | ||
Temporal Transfer | Spectral features | 0.72 | 0.41 | 0.68 |
Deep Features | 0.55 | 0.37 | 1.04 | |
Spatial Transfer | Spectral features | 0.43 | 0.48 | 0.42 |
Deep Features | 0.37 | 0.4 | 0.51 |
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Share and Cite
Lyu, H.; Lu, H.; Mou, L.; Li, W.; Wright, J.; Li, X.; Li, X.; Zhu, X.X.; Wang, J.; Yu, L.; et al. Long-Term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data. Remote Sens. 2018, 10, 471. https://doi.org/10.3390/rs10030471
Lyu H, Lu H, Mou L, Li W, Wright J, Li X, Li X, Zhu XX, Wang J, Yu L, et al. Long-Term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data. Remote Sensing. 2018; 10(3):471. https://doi.org/10.3390/rs10030471
Chicago/Turabian StyleLyu, Haobo, Hui Lu, Lichao Mou, Wenyu Li, Jonathon Wright, Xuecao Li, Xinlu Li, Xiao Xiang Zhu, Jie Wang, Le Yu, and et al. 2018. "Long-Term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data" Remote Sensing 10, no. 3: 471. https://doi.org/10.3390/rs10030471