Long-term annual mapping of four cities on different continents by applying a deep information learning method to Landsat data

Remote Sensing

Volumn 10, Issue 3, 2018, Pages

  Lyu, Haobo ^a   Lu, Hui ^a,b   Mou, Lichao ^c,d   Li, Wenyu ^a   Wright, Jonathon ^a,b   Li, Xuecao ^e   Li, Xinlu ^a,f   Zhu, Xiao Xiang ^c,d   Wang, Jie ^g   Yu, Le ^a,b   Gong, Peng ^a,b  

^a TSINGHUA UNIVERSITY   (China)

^b JOINT CENTER FOR GLOBAL CHANGE STUDIES   (China)

^c GERMAN AEROSPACE CENTER DLR   (Germany)

^d TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^e IOWA STATE UNIVERSITY   (United States)

^f CENTER FOR SPACE SCIENCE AND APPLIED RESEARCH   (China)

^g INSTITUTE OF REMOTE SENSING APPLICATIONS   (China)

Author keywords

Deep learning; Long time series; Recurrent neural network; Transfer learning; Urban mapping

Indexed keywords

LEARNING SYSTEMS; MAPPING; RECURRENT NEURAL NETWORKS; REMOTE SENSING; SAMPLING; SATELLITE IMAGERY; TIME SERIES;

ATMOSPHERIC CONDITIONS; LIMITED TRAINING DATA; LONG TIME SERIES; MAPPING OF URBAN AREAS; MASSIVE REMOTE SENSING DATUM; STATE-OF-THE-ART METHODS; TRANSFER LEARNING; URBAN MAPPING;

DEEP LEARNING;

EID: 85044202715     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10030471     Document Type: Article

References (56)

1. Grey, W.M.F.; Luckman, A.J.; Holland, D. Mapping urban change in the UK using satellite radar interferometry. Remote Sens. Environ. 2003, 87, 16-22
2. Schneider, A.; Friedl, M.A.; Potere, D. Mapping global urban areas using MODIS 500-m data: New methods and datasets based on 'urban ecoregions'. Remote Sens. Environ. 2010, 114, 1733-1746
3. Cao, X.; Chen, J.; Imura, H.; Higashi, O. A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data. Remote Sens. Environ. 2009, 113, 2205-2209
4. Foley, J.A.; Defries, R.; Asner, G.P.; Barford, C.; Bonan, G.; Carpenter, S.R.; Chapin, F.S.; Coe, M.T.; Daily, G.C.; Gibbs, H.K. Global consequences of land use. Science 2005, 309, 570-574
5. Grimm, N.B.; Faeth, S.H.; Golubiewski, N.E.; Redman, C.L.;Wu, J.; Bai, X.; Briggs, J.M. Global change and the ecology of cities. Science 2008, 319, 756-760
6. Yu, C.; Xiao, Y.; Ni, S. Changing patterns of urban-rural nutrient flows in China: Driving forces and options. Sci. Bull. 2017, 62, 83-91
7. Li, X.; Li, W.; Middel, A.; Harlan, S.L.; Brazel, A.J.; Ii, B.L.T. Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral-demographic-economic factors. Remote Sens. Environ. 2016, 174, 233-243
8. Lu, D.; Tian, H.; Zhou, G.; Ge, H. Regional mapping of human settlements in southeastern China with multisensor remotely sensed data. Remote Sens. Environ. 2008, 112, 3668-3679
9. Ma, T.; Zhou, C.; Tao, P.; Haynie, S.; Fan, J. Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities. Remote Sens. Environ. 2012, 124, 99-107
10. Mertes, C.M.; Schneider, A.; Sulla-Menashe, D.; Tatem, A.J.; Tan, B. Detecting change in urban areas at continental scales with MODIS data. Remote Sens. Environ. 2015, 158, 331-347
11. Boasson, E.; Howarth, P.J. Landsat digital enhancements for change detection in urban environments. Remote Sens. Environ. 1983, 13, 149-160
12. Gong, P.; Wang, J.; Yu, L.; Zhao, Y.; Zhao, Y.; Liang, L.; Niu, Z.; Huang, X.; Fu, H.; Liu, S. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens. 2013, 34, 2607-2654
13. Gong, P.; Howarth, P.J. The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. Photogramm. Eng. Remote Sens. 1990, 56, 67-73
14. Song, X.P.; Sexton, J.O.; Huang, C.; Channan, S.; Townshend, J.R. Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sens. Environ. 2016, 175, 1-13
15. Wang, L.; Li, C.C.; Ying, Q.; Xiao, C.; Wang, X.Y.; Li, X.Y.; Hu, L.Y.; Liang, L.; Yu, L.; Huabing, H.; et al. China's urban expansion from 1990 to 2010 determined with satellite remote sensing. Sci. Bull. 2012, 57, 2802-2812
16. Lambin, E.F.; Geist, H.J.; Lepers, E. Dynamics of Land-use and Land-Cover Change in Tropical Regions. Annu. Rev. Environ. Resour. 2003, 28, 205-241
17. Yang, L.; Huang, C.; Homer, C.G.;Wylie, B.K.; Coan, M.J. An approach for mapping large-area impervious surfaces: Synergistic use of Landsat-7 ETM+ and high spatial resolution imagery. Can. J. Remote Sens. 2003, 29, 230-240
18. Homer, C.; Huang, C.; Yang, L.; Wylie, B.; Coan, M. Development of a 2001 National Land-Cover Database for the United States. Photogramm. Eng. Remote Sens. 2004, 70, 829-840
19. Schneider, A.; Mertes, C.M. Expansion and growth in Chinese cities, 1978-2010. Environ. Res. Lett. 2014, 9, 024008
20. Sexton, J.O.; Song, X.P.; Huang, C.; Channan, S.; Baker, M.E.; Townshend, J.R. Urban growth of the Washington, D.C.-Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover. Remote Sens. Environ. 2013, 129, 42-53
21. Li, X.; Gong, P.; Liang, L. A 30-year (1984-2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sens. Environ. 2015, 116, 78-90
22. Feyisa, G.L.; Meilby, H.; Jenerette, G.D.; Pauliet, S. Locally optimized separability enhancement indices for urban land cover mapping: Exploring thermal environmental consequences of rapid urbanization in Addis Ababa, Ethiopia. Remote Sens. Environ. 2016, 175, 14-31
23. Shahtahmassebi, A.R.; Lin, Y.; Lin, L.; Atkinson, P.M.; Moore, N.; Wang, K.; He, S.; Huang, L.; Wu, J.; Shen, Z. Reconstructing Historical Land Cover Type and Complexity by Synergistic Use of Landsat Multispectral Scanner and CORONA. Remote Sens. 2017, 9, 682
24. Yang, C.; Wu, G.; Ding, K.; Shi, T.; Li, Q.; Wang, J. Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods. Remote Sens. 2017, 9, 1222
25. Arai, K. A supervised Thematic Mapper classification with a purification of training samples. Int. J. Remote Sens. 2007, 13, 2039-2049
26. Schneider, A. Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sens. Environ. 2012, 124, 689-704
27. Lecun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436-444
28. Yosinski, J.; Clune, J.; Bengio, Y.; Lipson, H. How transferable are features in deep neural networks? Adv. Neural Inf. Process. Syst. 2014, 27, 3320-3328
29. Marmanis, D.; Datcu, M.; Esch, T.; Stilla, U. Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks. IEEE Geosci. Remote Sens. Lett. 2016, 13, 105-109
30. Jean, N.; Burke, M.; Xie, M.; Davis, W.M.; Lobell, D.B.; Ermon, S. Combining satellite imagery and machine learning to predict poverty. Science 2016, 353, 790-794
31. Zhu, X.X.; Tuia, D.; Mou, L.; Xia, G.S.; Zhang, L.; Xu, F.; Fraundorfer, F. Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources. IEEE Geosci. Remote Sens. Mag. 2018, 5, 8-36
32. Lyu, H.; Lu, H.; Mou, L. Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection. Remote Sens. 2016, 8, 506
33. Masek, J.G.; Vermote, E.F.; Saleous, N.E.; Wolfe, R.; Hall, F.G.; Huemmrich, K.F.; Gao, F.; Kutler, J.; Lim, T.K. A Landsat surface reflectance dataset for North America, 1990-2000. IEEE Geosci. Remote Sens. Lett. 2006, 3, 68-72
34. Zhu, Z.; Woodcock, C.E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 2012, 118, 83-94
35. Cracknell, A.P.; Kanniah, K.D.; Tan, K.P.; Wang, L. Evaluation of MODIS gross primary productivity and land cover products for the humid tropics using oil palm trees in Peninsular Malaysia and Google Earth imagery. Int. J. Remote Sens. 2013, 34, 7400-7423
36. Cracknell, A.P.; Kanniah, K.D.; Tan, K.P.; Lei, W. Towards the development of a regional version of MOD17 for the determination of gross and net primary productivity of oil palm trees. Int. J. Remote Sens. 2015, 36, 262-289
37. Xian, G.; Homer, C.; Dewitz, J.; Fry, J.; Hossain, N.;Wickham, J. Change of Impervious Surface Area Between 2001 and 2006 in the Conterminous United States. Photogramm. Eng. Remote Sens. 2011, 77, 758-762
38. Pan, S.J.; Yang, Q. A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng. 2010, 22, 1345-1359
39. Xie, M.; Jean, N.; Burke, M.; Lobell, D.; Ermon, S. Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. arXiv 2015
40. Mou, L.; Ghamisi, P.; Zhu, X.X. Deep Recurrent Neural Networks for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens. 2017, 55, 3639-3655
41. Cohen, I.; Cozman, F.G.; Sebe, N.; Cirelo, M.C.; Huang, T.S. Semisupervised learning of classifiers: Theory, algorithms, and their application to human-computer interaction. IEEE Trans. Pattern Anal. 2004, 26, 1553-1566
42. Tüske, Z.; Tahir, M.A.; Schlüter, R.; Ney, H. Integrating Gaussian mixtures into deep neural networks: Softmax layer with hidden variables. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, South Brisbane, Australia, 19-24 April 2015; pp. 4285-4289
43. Tieleman, T.; Hinton, G.G. Lecture 6.5-RmsProp: Divide the gradient by a running average of its recent magnitude. Neural Netw. Mach. Learn. 2012, 4, 26-31
44. Shi, L.; Ling, F.; Ge, Y.; Foody, G.M.; Li, X.; Wang, L.; Zhang, Y.; Du, Y. Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study inWuhan City Using Landsat Time-Series Datasets from 1987 to 2016. Remote Sens. 2017, 9, 1148
45. Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5-32
46. Cutler, A.; Cutler, D.R.; Stevens, J.R. Random Forests. Mach. Learn. 2004, 45, 157-176
47. Tong, S.; Koller, D. Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2001, 2, 45-66
48. Chang, C.C.; Lin, C.J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2
49. Mikolov, T.; Karafiát, M.; Burget, L.; Cernocký, J.; Khudanpur, S. Recurrent neural network based language model. In Proceedings of the 11th Annual Conference of the International Speech Communication Association, Chiba, Japan, 26-30 September 2010; pp. 1045-1048
50. Graves, A. Long Short-Term Memory. Neural Comput. 2014, 9, 1735-1780
51. Li, C.;Wang, J.;Wang, L.; Hu, L.; Gong, P. Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery. Remote Sens. 2014, 6, 964-983
52. Li, C.; Gong, P.;Wang, J.; Zhu, Z.; Biging, G.S.; Yuan, C.; Hu, T.; Zhang, H.;Wang, Q.; Li, X. The first all-season sample set for mapping global land cover with Landsat-8 data. Sci. Bull. 2017, 7, 508-515
53. Bartholomew, D.J.; Steele, F.; Galbraith, J.I.; Moustaki, I. Analysis of multivariate social science data. Struct. Equ. Model. Multidiscip. J. 2011, 18, 686-693
54. Mcgilvray, A. Sydney & Melbourne: A tale of two cities. Nature 2016, S58-S65
55. Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18-27
56. Zhang, Q.; Li, B.; Thau, D.; Moore, R. Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery. Remote Sens. 2015, 2015, 11887-11913