Deep learning based multi-temporal crop classification

Remote Sensing of Environment

Volumn 221, Issue , 2019, Pages 430-443

  Zhong, Liheng ^a   Hu, Lina ^b   Zhou, Hang ^c  

^a STATE OF CALIFORNIA   (United States)

^b TSINGHUA UNIVERSITY   (China)

^c Descartes Labs Inc   (United States)

Author keywords

Artificial neural network; Convolutional neural network; Crop classification; Deep learning; Landsat; Multi temporal classification

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVOLUTION; CROPS; CULTIVATION; DECISION TREES; LONG SHORT-TERM MEMORY; NEURAL NETWORKS; TIME SERIES; WATER MANAGEMENT;

AUTOMATED FEATURE EXTRACTION; CLASSIFICATION FRAMEWORK; CLASSIFICATION MODELS; CONVOLUTIONAL NEURAL NETWORK; CROP CLASSIFICATION; ENHANCED VEGETATION INDEX; LANDSAT; MULTITEMPORAL CLASSIFICATION;

DEEP LEARNING;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; EXPERIMENTAL STUDY; FARMING SYSTEM; LANDSAT; MACHINE LEARNING; NUMERICAL MODEL; REMOTE SENSING; SUMMER; TIME SERIES ANALYSIS; VEGETATION CLASSIFICATION; VEGETATION INDEX; VISUALIZATION;

CALIFORNIA; UNITED STATES; YOLO COUNTY;

EID: 85057535465     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2018.11.032     Document Type: Article

References (115)

1. Audebert, N., Le Saux, B., Lefèvre, S., Beyond RGB: very high resolution urban remote sensing with multimodal deep networks. ISPRS J. Photogramm. Remote Sens. 140 (2018), 20–32.
2. Badhwar, G.B., Automatic corn-soybean classification using Landsat MSS data. II. Early season crop proportion estimation. Remote Sens. Environ. 14 (1984), 31–37.
3. Beck, P.S.A., Atzberger, C., Høgda, K.A., Johansen, B., Skidmore, A.K., Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sens. Environ. 100 (2006), 321–334.
4. Bergstra, J., Bengio, Y., Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13 (2012), 281–305.
5. Bradley, B.A., Mustard, J.F., Comparison of phenology trends by land cover class: a case study in the Great Basin, USA. Glob. Chang. Biol. 14 (2008), 334–346.
6. Bradley, B.A., Jacob, R.W., Hermance, J.F., Mustard, J.F., A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sens. Environ. 106 (2007), 137–145.
7. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A., Classification and Regression Trees. 1984, CRC press.
8. Brown, M.E., de Beurs, K., Vrieling, A., The response of African land surface phenology to large scale climate oscillations. Remote Sens. Environ. 114 (2010), 2286–2296.
9. Carrão, H., Gonçalves, P., Caetano, M., Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sens. Environ. 112 (2008), 986–997.
10. CDWR, California water plan, update 2009. California Department of Water Resources, Bulletin, 2009, 160–169.
11. Chapelle, O., Haffner, P., Vapnik, V.N., Support vector machines for histogram-based image classification. IEEE Trans. Neural Netw. 10 (1999), 1055–1064.
12. Chen, T., Guestrin, C., XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, 785–794.
13. Chen, J., Jonsson, P., Tamura, M., Gu, Z., Matsushita, B., Eklundh, L., A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sens. Environ. 91 (2004), 332–344.
14. Chen, X., Xiang, S., Liu, C.-., Pan, C.-., Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 11 (2014), 1797–1801.
15. Chen, Y., Lin, Z., Zhao, X., Wang, G., Gu, Y., Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 7 (2014), 2094–2107.
16. Clancey, W.J., The epistemology of a rule-based expert system—a framework for explanation. Artif. Intell. 20 (1983), 215–251.
17. Connor, J.T., Martin, R.D., Atlas, L.E., Recurrent neural networks and robust time series prediction. IEEE Trans. Neural Netw. 5 (1994), 240–254.
18. Cortes, C., Vapnik, V., Support-vector networks. Mach. Learn. 20 (1995), 273–297.
19. Dannenberg, M.P., Song, C., Hwang, T., Wise, E.K., Empirical evidence of El Niño–Southern Oscillation influence on land surface phenology and productivity in the western United States. Remote Sens. Environ. 159 (2015), 167–180.
20. Dong, J., Xiao, X., Kou, W., Qin, Y., Zhang, G., Li, L., et al. Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms. Remote Sens. Environ. 160 (2015), 99–113.
21. Evans, J.P., Geerken, R., Classifying rangeland vegetation type and coverage using a Fourier component based similarity measure. Remote Sens. Environ. 105 (2006), 1–8.
22. Fernandez-Delgado, M., Cernadas, E., Barro, S., Amorim, D., Do we need hundreds of classifiers to solve real world classification problems?. J. Mach. Learn. Res. 15 (2014), 3133–3181.
23. Fisher, J.I., Mustard, J.F., Cross-scalar satellite phenology from ground, Landsat, and MODIS data. Remote Sens. Environ. 109 (2007), 261–273.
24. Fisher, J.I., Mustard, J.F., Vadeboncoeur, M.A., Green leaf phenology at Landsat resolution: scaling from the field to the satellite. Remote Sens. Environ. 100 (2006), 265–279.
25. Friedl, M.A., Brodley, C.E., Strahler, A.H., Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Trans. Geosci. Remote Sens. 37 (1999), 969–977.
26. Funk, C., Budde, M.E., Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe. Remote Sens. Environ. 113 (2009), 115–125.
27. Galford, G.L., Mustard, J.F., Melillo, J., Gendrin, A., Cerri, C.C., Cerri, C.E.P., Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil. Remote Sens. Environ. 112 (2008), 576–587.
28. Geerken, R.A., An algorithm to classify and monitor seasonal variations in vegetation phenologies and their inter-annual change. ISPRS J. Photogramm. Remote Sens. 64 (2009), 422–431.
29. Gonsamo, A., Chen, J.M., Circumpolar vegetation dynamics product for global change study. Remote Sens. Environ. 182 (2016), 13–26.
30. Guidici, D., Clark, M.L., One-dimensional convolutional neural network land-cover classification of multi-seasonal hyperspectral imagery in the San Francisco Bay Area, California. Remote Sens., 9, 2017, 629.
31. Hochreiter, S., Schmidhuber, J., Long short-term memory. Neural Comput. 9 (1997), 1735–1780.
32. Hu, W., Huang, Y., Wei, L., Zhang, F., Li, H., Deep convolutional neural networks for hyperspectral image classification. J. Sens., 2015, 2015.
33. Hu, F., Xia, G., Hu, J., Zhang, L., Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens. 7 (2015), 14680–14707.
34. Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X., Ferreira, L., Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83 (2002), 195–213.
35. Jia, X., Khandelwal, A., Nayak, G., Gerber, J., Carlson, K., West, P., et al. Incremental dual-memory LSTM in land cover prediction. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, 867–876.
36. Jönsson, P., Eklundh, L., TIMESAT—a program for analyzing time-series of satellite sensor data. Comput. Geosci. 30 (2004), 833–845.
37. Kampffmeyer, M., Salberg, A., Jenssen, R., Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional. Neural Netw., 2016, 1–9.
38. Kandasamy, S., Baret, F., Verger, A., Neveux, P., Weiss, M., A comparison of methods for smoothing and gap filling time series of remote sensing observations–application to MODIS LAI products. Biogeosciences 10 (2013), 4055–4071.
39. Kingma, D.P., Ba, J., Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
40. Krizhevsky, A., Sutskever, I., Hinton, G.E., ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Proces. Syst. 2 (2012), 1097–1105.
41. Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A., Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sens. Lett. 14 (2017), 778–782.
42. Lawrence, R.L., Wood, S.D., Sheley, R.L., Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest). Remote Sens. Environ. 100 (2006), 356–362.
43. LeCun, Y., Bengio, Y., Convolutional networks for images, speech, and time-series. Anonymous, (eds.) The Handbook of Brain Theory and Neural Networks, 1995.
44. Li, W., Fu, H., Yu, L., Gong, P., Feng, D., Li, C., et al. Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping. Int. J. Remote Sens. 37 (2016), 5632–5646.
45. Li, Y., Zhang, H., Shen, Q., Spectral–spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens., 9, 2017, 67.
46. Li, W., Fu, H., Yu, L., Cracknell, A., Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sens., 9, 2017.
47. Liaw, A., Wiener, M., Classification and regression by randomForest. R News 2 (2002), 18–22.
48. Lloyd, D., A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. Int. J. Remote Sens. 11 (1990), 2269–2279.
49. Lyu, H., Lu, H., Mou, L., Learning a transferable change rule from a recurrent neural network for land cover change detection. Remote Sens., 8, 2016, 506.
50. Lyu, H., Lu, H., Mou, L., Li, W., Wright, J., Li, X., et al. Long-term annual mapping of four cities on different continents by applying a deep information learning method to Landsat data. Remote Sens., 10, 2018, 471.
51. Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P., High-resolution aerial image labeling with convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 55 (2017), 7092–7103.
52. Marcos, D., Volpi, M., Kellenberger, B., Tuia, D., Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models. ISPRS J. Photogramm. Remote Sens. 145 (2018), 96–107.
53. Marmanis, D., Schindler, K., Wegner, J.D., Galliani, S., Datcu, M., Stilla, U., Classification with an edge: improving semantic image segmentation with boundary detection. ISPRS J. Photogramm. Remote Sens. 135 (2018), 158–172.
54. Mou, L., Zhu, X.X., RiFCN: recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images. arXiv preprint arXiv:1805.02091, 2018.
55. Mou, L., Ghamisi, P., Zhu, X.X., Unsupervised spectral-spatial feature learning via deep residual conv-deconv network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens., 2018.
56. Mou, L., Bruzzone, L., Zhu, X.X., Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery. arXiv preprint arXiv:1803.02642, 2018.
57. Na, X., Zhang, S., Li, X., Yu, H., Liu, C., Improved land cover mapping using random forests combined with Landsat thematic mapper imagery and ancillary geographic data. Photogramm. Eng. Remote. Sens. 76 (2010), 833–840.
58. Olsson, L., Eklundh, L., Fourier-series for analysis of temporal sequences of satellite sensor imagery. Int. J. Remote Sens. 15 (1994), 3735–3741.
59. Penatti, O.A.B., Nogueira, K., Dos Santos, J.A., Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2015-October, 2015, 44–51.
60. Reed, B.C., Brown, J.F., Vanderzee, D., Loveland, T.R., Merchant, J.W., Ohlen, D.O., Measuring phenological variability from satellite imagery. J. Veg. Sci. 5 (1994), 703–714.
61. Ripley, B.D., Pattern Recognition and Neural Networks. 2007, Cambridge university press.
62. Roerink, G.J., Menenti, M., Verhoef, W., Reconstructing cloudfree NDVI composites using Fourier analysis of time series. Int. J. Remote Sens. 21 (2000), 1911–1917.
63. Rogan, J., Franklin, J., Roberts, D.A., A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery. Remote Sens. Environ. 80 (2002), 143–156.
64. Rußwurm, M., Körner, M., Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satellite images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017.
65. Rußwurm, M., Körner, M., Multi-temporal land cover classification with sequential recurrent encoders. ISPRS Int. J. Geoinf., 7, 2018, 129.
66. Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., Ohno, H., A crop phenology detection method using time-series MODIS data. Remote Sens. Environ. 96 (2005), 366–374.
67. Sakamoto, T., Van Nguyen, N., Ohno, H., Ishitsuka, N., Yokozawa, M., Spatio–temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sens. Environ. 100 (2006), 1–16.
68. Sakamoto, T., Wardlow, B.D., Gitelson, A.A., Verma, S.B., Suyker, A.E., Arkebauer, T.J., A two-step filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sens. Environ. 114 (2010), 2146–2159.
69. Sakamoto, T., Wardlow, B.D., Gitelson, A.A., Detecting spatiotemporal changes of corn developmental stages in the U.S. Corn Belt using MODIS WDRVI data. IEEE Trans. Geosci. Remote Sens. 49 (2011), 1926–1936.
70. Sakamoto, T., Gitelson, A.A., Arkebauer, T.J., MODIS-based corn grain yield estimation model incorporating crop phenology information. Remote Sens. Environ. 131 (2013), 215–231.
71. Sakamoto, T., Gitelson, A.A., Arkebauer, T.J., Near real-time prediction of U.S. corn yields based on time-series MODIS data. Remote Sens. Environ. 147 (2014), 219–231.
72. Shao, Y., Lunetta, R.S., Wheeler, B., Iiames, J.S., Campbell, J.B., An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data. Remote Sens. Environ. 174 (2016), 258–265.
73. Sherrah, J., Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery. arXiv preprint arXiv:1606.02585, 2016.
74. Shi, D., Yang, X., An assessment of algorithmic parameters affecting image classification accuracy by random forests. Photogramm. Eng. Remote. Sens. 82 (2016), 407–417.
75. Siachalou, S., Mallinis, G., Tsakiri-Strati, M., A hidden Markov models approach for crop classification: linking crop phenology to time series of multi-sensor remote sensing data. Remote Sens., 7, 2015.
76. Simonneaux, V., Francois, P., Identifying main crop classes in an irrigated area using high resolution image time series. Geoscience and Remote Sensing Symposium, 2003. IGARSS ‘03. Proceedings. 2003 IEEE International, vol. 1, 2003, 252–254.
77. Simonneaux, V., Duchemin, B., Helson, D., Er-Raki, S., Olioso, A., Chehbouni, A.G., The use of high-resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco. Int. J. Remote Sens. 29 (2008), 95–116.
78. Soudani, K., le Maire, G., Dufrene, E., Francois, C., Delpierre, N., Ulrich, E., et al. Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remote Sens. Environ. 112 (2008), 2643–2655.
79. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15 (2014), 1929–1958.
80. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. Going deeper with convolutions. Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2015, 1–9.
81. Verhoef, W., Application of harmonic analysis of NDVI time series (HANTS). Fourier Analysis of Temporal NDVI in the Southern African and American Continents, 108, 1996, 19–24.
82. Verhoef, W., Menenti, M., Azzali, S., A colour composite of NOAA-AVHRR-NDVI based on time series analysis (1981–1992). Int. J. Remote Sens. 17 (1996), 231–235.
83. Vicente-Guijalba, F., Martinez-Marin, T., Lopez-Sanchez, J.M., Crop phenology estimation using a multitemporal model and a Kalman filtering strategy. IEEE Geosci. Remote Sens. Lett. 11 (2014), 1081–1085.
84. Vina, A., Gitelson, A.A., Rundquist, D.C., Keydan, G., Leavitt, B., Schepers, J., Remote sensing - monitoring maize (Zea mays L.) phenology with remote sensing. Agron. J. 96 (2004), 1139–1147.
85. Volpi, M., Tuia, D., Dense semantic labeling of subdecimeter resolution images with convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 55 (2017), 881–893.
86. Walker, J.J., de Beurs, K.M., Wynne, R.H., Dryland vegetation phenology across an elevation gradient in Arizona, USA, investigated with fused MODIS and Landsat data. Remote Sens. Environ. 144 (2014), 85–97.
87. Walker, J.J., de Beurs, K.M., Henebry, G.M., Land surface phenology along urban to rural gradients in the U.S. Great Plains. Remote Sens. Environ. 165 (2015), 42–52.
88. Wan, X., Zhao, C., Wang, Y., Liu, W., Stacked sparse autoencoder in hyperspectral data classification using spectral-spatial, higher order statistics and multifractal spectrum features. Infrared Phys. Technol. 86 (2017), 77–89.
89. Wang, H., Chen, J., Wu, Z., Lin, H., Rice heading date retrieval based on multi-temporal MODIS data and polynomial fitting. Int. J. Remote Sens. 33 (2012), 1905–1916.
90. Wardlow, B.D., Egbert, S.L., Large-area crop mapping using time-series MODIS 250 m NDVI data: an assessment for the US Central Great Plains. Remote Sens. Environ. 112 (2008), 1096–1116.
91. WWW1, U.S. Geological Survey Science (USGS) Land Satellites Data System (LSDS) research and development. https://espa.cr.usgs.gov/. (Accessed 22 October 2018)
92. WWW2, Keras: the Python deep learning library. https://keras.io/. (Accessed 22 October 2018)
93. WWW3, Tensorflow: an open source software library for high performance numerical computation. https://www.tensorflow.org. (Accessed 22 October 2018)
94. WWW4, Scikit-learn: machine learning in Python. http://scikit-learn.org/. (Accessed 22 October 2018)
95. WWW5, XGBoost documentation, Python API reference. https://xgboost.readthedocs.io/en/latest/python/python_api.html. (Accessed 22 October 2018)
96. Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., et al. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sens. Environ. 95 (2005), 480–492.
97. Xiao, X., Boles, S., Frolking, S., Li, C., Babu, J.Y., Salas, W., et al. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens. Environ. 100 (2006), 95–113.
98. Xie, Y., Sha, Z., Yu, M., Remote sensing imagery in vegetation mapping: a review. J. Plant Ecol. 1 (2008), 9–23.
99. Xin, Q., Broich, M., Zhu, P., Gong, P., Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics. Remote Sens. Environ. 161 (2015), 63–77.
100. Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H., Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579, 2015.
101. Yue, J., Zhao, W., Mao, S., Liu, H., Spectral-spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sens. Lett. 6 (2015), 468–477.
102. Zaremba, W., Sutskever, I., Vinyals, O., Recurrent neural network regularization. arXiv preprint arXiv:1409.2329, 2014.
103. Zeiler, M.D., Fergus, R., Visualizing and understanding convolutional networks. European Conference on Computer Vision, 2014, 818–833.
104. Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84 (2003), 471–475.
105. Zhang, M., Zhou, Q., Chen, Z., Liu, J., Zhou, Y., Cai, C., Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data. Int. J. Appl. Earth Obs. Geoinf. 10 (2008), 476–485.
106. Zhang, J., Feng, L., Yao, F., Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information. ISPRS J. Photogramm. Remote Sens. 94 (2014), 102–113.
107. Zhang, G., Xiao, X., Dong, J., Kou, W., Jin, C., Qin, Y., et al. Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data. ISPRS J. Photogramm. Remote Sens. 106 (2015), 157–171.
108. Zhao, W., Du, S., Spectral–spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Trans. Geosci. Remote Sens. 54 (2016), 4544–4554.
109. Zhong, L., Hawkins, T., Biging, G., Gong, P., A phenology-based approach to map crop types in the San Joaquin Valley, California. Int. J. Remote Sens. 32 (2011), 7777–7804.
110. Zhong, L., Gong, P., Biging, G.S., Phenology-based crop classification algorithm and its implications on agricultural water use assessments in California's central valley. Photogramm. Eng. Remote. Sens. 78 (2012), 799–813.
111. Zhong, L., Gong, P., Biging, G.S., Efficient corn and soybean mapping with temporal extendability: a multi-year experiment using Landsat imagery. Remote Sens. Environ. 140 (2014), 1–13.
112. Zhong, L., Hu, L., Yu, L., Gong, P., Biging, G.S., Automated mapping of soybean and corn using phenology. ISPRS J. Photogramm. Remote Sens. 119 (2016), 151–164.
113. Zhong, L., Yu, L., Li, X., Hu, L., Gong, P., Rapid corn and soybean mapping in US Corn Belt and neighboring areas. Sci. Rep., 6, 2016, 36240.
114. Zhu, X.X., Tuia, D., Mou, L., Xia, G., Zhang, L., Xu, F., et al. Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci. Remote Sens. Mag. 5 (2017), 8–36.
115. Zou, Q., Ni, L., Zhang, T., Wang, Q., Deep learning based feature selection for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 12 (2015), 2321–2325.