Estimating biomass and nitrogen amount of barley and grass using UAV and aircraft based spectral and photogrammetric 3D features

Remote Sensing

Volumn 10, Issue 7, 2018, Pages

  Näsi, Roope ^a   Viljanen, Niko ^a   Kaivosoja, Jere ^b   Alhonoja, Katja ^c   Hakala, Teemu ^a   Markelin, Lauri ^a   Honkavaara, Eija ^a  

^a FINNISH GEOSPATIAL RESEARCH INSTITUTE   (Finland)

^b FINNISH FOREST RESEARCH INSTITUTE   (Finland)

^c Yara Kotkaniemi Research Station   (Finland)

Author keywords

Biomass; Drone; Hyperspectral; Machine learning; Nitrogen; Photogrammetry; Precision agriculture; Random forest; Regression; UAV

Indexed keywords

BIOMASS; CAMERAS; CORRELATION METHODS; CROPS; DECISION TREES; DRONES; ESTIMATION; FABRY-PEROT INTERFEROMETERS; LEARNING SYSTEMS; MEAN SQUARE ERROR; PHOTOGRAMMETRY; PRECISION AGRICULTURE; REMOTE SENSING; UNMANNED AERIAL VEHICLES (UAV);

HIGH SPATIAL RESOLUTION; HYPERSPECTRAL; PEARSON CORRELATION COEFFICIENTS; RADIOMETRIC CALIBRATIONS; RANDOM FORESTS; REGRESSION; ROOT MEAN SQUARE ERRORS; SIMPLE LINEAR REGRESSION;

NITROGEN;

EID: 85050487335     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10071082     Document Type: Article

References (81)

1. Yang, G.; Liu, J.; Zhao, C.; Li, Z.; Huang, Y.; Yu, H.; Xu, B.; Yang, X.; Zhu, D.; Zhang, X.; et al. Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives. Front. Plant Sci. 2017, 8
2. Balafoutis, A.; Beck, B.; Fountas, S.; Vangeyte, J.; Wal, T.; van der Soto, I.; Gómez-Barbero, M.; Barnes, A.; Eory, V. Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics. Sustainability 2017, 9, 1339
3. Mulla, D.J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. 2013, 114, 358-371
4. Sankaran, S.; Khot, L.R.; Espinoza, C.Z.; Jarolmasjed, S.; Sathuvalli, V.R.; Vandemark, G.J.; Miklas, P.N.; Carter, A.H.; Pumphrey, M.O.; Knowles, N.R.; et al. Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. Eur. J. Agron. 2015, 70, 112-123
5. Saari, H.; Pellikka, I.; Pesonen, L.; Tuominen, S.; Heikkilä, J.; Holmlund, C.; Mäkynen, J.; Ojala, K.; Antila, T. Unmanned aerial vehicle (UAV) operated spectral camera system for forest and agriculture applications. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII; International Society for Optics and Photonics: Bellingham, WA, USA, 2011; Volume 8174, p. 81740H
6. Mäkynen, J.; Holmlund, C.; Saari, H.; Ojala, K.; Antila, T. Unmanned aerial vehicle (UAV) operated megapixel spectral camera. In Electro-Optical Remote Sensing, Photonic Technologies, and Applications V; International Society for Optics and Photonics: Bellingham, WA, USA, 2011; Volume 8186, p. 81860Y
7. Honkavaara, E.; Saari, H.; Kaivosoja, J.; Pölönen, I.; Hakala, T.; Litkey, P.; Mäkynen, J.; Pesonen, L. Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sens. 2013, 5, 5006-5039
8. Oliveira, R.A.; Tommaselli, A.M.G.; Honkavaara, E. Geometric calibration of a hyperspectral frame camera. Photogramm. Rec. 2016, 31, 325-347
9. Näsi, R.; Honkavaara, E.; Lyytikäinen-Saarenmaa, P.; Blomqvist, M.; Litkey, P.; Hakala, T.; Viljanen, N.; Kantola, T.; Tanhuanpää, T.; Holopainen, M. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sens. 2015, 7, 15467-15493
10. Honkavaara, E.; Eskelinen, M.A.; Pölönen, I.; Saari, H.; Ojanen, H.; Mannila, R.; Holmlund, C.; Hakala, T.; Litkey, P.; Rosnell, T.; et al. Remote sensing of 3-D geometry and surface moisture of a peat production area using hyperspectral frame cameras in visible to short-wave infrared spectral ranges onboard a small unmanned airborne vehicle (UAV). IEEE Trans. Geosci. Remote Sens. 2016, 54, 5440-5454
11. Nevalainen, O.; Honkavaara, E.; Tuominen, S.; Viljanen, N.; Hakala, T.; Yu, X.; Hyyppä, J.; Saari, H.; Pölönen, I.; Imai, N.N.; et al. Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sens. 2017, 9, 185
12. Näsi, R.; Honkavaara, E.; Blomqvist, M.; Lyytikäinen-Saarenmaa, P.; Hakala, T.; Viljanen, N.; Kantola, T.; Holopainen, M. Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban For. Urban Green. 2018, 30, 72-83
13. Saarinen, N.; Vastaranta, M.; Näsi, R.; Rosnell, T.; Hakala, T.; Honkavaara, E.; Wulder, M.A.; Luoma, V.; Tommaselli, A.M.G.; Imai, N.N.; et al. Assessing biodiversity in boreal forests with UAV-Based photogrammetric point clouds and hyperspectral imaging. Remote Sens. 2018, 10, 338
14. Pölönen, I.; Saari, H.; Kaivosoja, J.; Honkavaara, E.; Pesonen, L. Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XV; International Society for Optics and Photonics: Bellingham, WA, USA, 2013; Volume 8887, p. 88870J
15. Kaivosoja, J.; Pesonen, L.; Kleemola, J.; Pölönen, I.; Salo, H.; Honkavaara, E.; Saari, H.; Mäkynen, J.; Rajala, A. A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XV; International Society for Optics and Photonics: Bellingham, WA, USA, 2013; Volume 8887, p. 88870H
16. Bareth, G.; Aasen, H.; Bendig, J.; Gnyp, M.L.; Bolten, A.; Jung, A.; Michels, R.; Soukkamäki, J. Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements. 2015. Available online: http://www.ingentaconnect.com/content/ schweiz/pfg/2015/00002015/00000001/art00007 (accessed on 15 May 2018)
17. Moriya, é.A.S.; Imai, N.N.; Tommaselli, A.M.G.; Miyoshi, G.T. Mapping Mosaic Virus in Sugarcane Based on Hyperspectral Images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 10, 740-748
18. Kaivosoja, J.; Näsi, R.; Hakala, T.; Viljanen, N.; Honkavaara, E. Applying Different Remote Sensing Data to Determine Relative Biomass Estimations of Cereals for Precision Fertilization Task Generation. In Proceedings of the 8th International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2017), Crete Island, Greece, 21-24 September 2017
19. Jakob, S.; Zimmermann, R.; Gloaguen, R. The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo-A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data. Remote Sens. 2017, 9
20. Tuominen, S.; Näsi, R.; Honkavaara, E.; Balazs, A.; Hakala, T.; Viljanen, N.; Pölönen, I.; Saari, H.; Ojanen, H. Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity. Remote Sens. 2018, 10, 714
21. Aasen, H.; Burkart, A.; Bolten, A.; Bareth, G. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS J. Photogramm. Remote Sens. 2015, 108, 245-259
22. Oliveira, R.A. Generation of Hyperspectral Digital Surface Model in Forest Areas Using Hyperspectral 2D Frame Camera Onboard RPAS. Ph.D. Thesis, Department of Cartography, São Paulo State University, Presidente Prudente, Brazil, 2017
23. Bendig, J.; Yu, K.; Aasen, H.; Bolten, A.; Bennertz, S.; Broscheit, J.; Gnyp, M.L.; Bareth, G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 2015, 39, 79-87
24. Yue, J.; Yang, G.; Li, C.; Li, Z.; Wang, Y.; Feng, H.; Xu, B. Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models. Remote Sens. 2017, 9, 708
25. Viljanen, N.; Honkavaara, E.; Näsi, R.; Hakala, T.; Niemeläinen, O.; Kaivosoja, J. A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone. Agriculture 2018, 8, 70
26. Lumme, J.; Karjalainen, M.; Kaartinen, H.; Kukko, A.; Hyyppä, J.; Hyyppä, H.; Jaakkola, A.; Kleemola, J. Terrestrial laser scanning of agricultural crops. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2008, 37, 563-566
27. Hoffmeister, D.;Waldhoff, G.; Curdt, C.; Tilly, N.; Bendig, J.; Bareth, G. Spatial variability detection of crop height in a single field by terrestrial laser scanning. In Precision Agriculture 13; Wageningen Academic Publishers: Wageningen, The Netherlands, 2013; pp. 267-274. ISBN 978-90-8686-778-3
28. Tilly, N.; Hoffmeister, D.; Cao, Q.; Huang, S.; Lenz-Wiedemann, V.; Miao, Y.; Bareth, G. Multitemporal crop surface models: Accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. J Appl. Remote Sens. 2014, 8, 083671
29. Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693-712
30. Bendig, J.; Bolten, A.; Bareth, G. UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop Growth Variability. Photogramm. Fernerkund. Geoinf. 2013, 551-562
31. Bendig, J.; Bolten, A.; Bennertz, S.; Broscheit, J.; Eichfuss, S.; Bareth, G. Estimating Biomass of Barley Using Crop SurfaceModels (CSMs) Derived from UAV-Based RGB Imaging. Remote Sens. 2014, 6, 10395-10412
32. Li, W.; Niu, Z.; Chen, H.; Li, D.;Wu, M.; Zhao, W. Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecol. Indic. 2016, 67, 637-648
33. Berni, J.A.J.; Zarco-Tejada, P.J.; Suarez, L.; Fereres, E. Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle. IEEE Trans. Geosci. Remote Sens. 2009, 47, 722-738
34. Hunt, E.R.; Hively, W.D.; Fujikawa, S.J.; Linden, D.S.; Daughtry, C.S.T.; McCarty, G.W. Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring. Remote Sens. 2010, 2, 290-305
35. Candiago, S.; Remondino, F.; De Giglio, M.; Dubbini, M.; Gattelli, M. Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images. Remote Sens. 2015, 7, 4026-4047
36. Caturegli, L.; Corniglia, M.; Gaetani, M.; Grossi, N.; Magni, S.; Migliazzi, M.; Angelini, L.; Mazzoncini, M.; Silvestri, N.; Fontanelli, M.; et al. Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses. PLoS ONE 2016, 11
37. Geipel, J.; Link, J.;Wirwahn, J.A.; Claupein, W. A Programmable Aerial Multispectral Camera System for in-Season Crop Biomass and Nitrogen Content Estimation. Agriculture 2016, 6
38. Capolupo, A.; Kooistra, L.; Berendonk, C.; Boccia, L.; Suomalainen, J. Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches. ISPRS Int. J. Geo-Inf. 2015, 4, 2792-2820
39. Zarco-Tejada, P.J.; Guillén-Climent, M.L.; Hernández-Clemente, R.; Catalina, A.; González, M.R.; Martín, P. Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agric. For. Meteorol. 2013, 171-l72, 281-294
40. Reddersen, B.; Fricke, T.; Wachendorf, M. A multi-sensor approach for predicting biomass of extensively managed grassland. Comput. Electron. Agric. 2014, 109, 247-260
41. Fricke, T.; Wachendorf, M. Combining ultrasonic sward height and spectral signatures to assess the biomass of legume-grass swards. Comput. Electron. Agric. 2013, 99, 236-247
42. Schirrmann, M.; Giebel, A.; Gleiniger, F.; Pflanz, M.; Lentschke, J.; Dammer, K.-H. Monitoring Agronomic Parameters ofWinter Wheat Crops with Low-Cost UAV Imagery. Remote Sens. 2016, 8
43. Fassnacht, F.E.; Hartig, F.; Latifi, H.; Berger, C.; Hernández, J.; Corvalán, P.; Koch, B. Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sens. Environ. 2014, 154, 102-114
44. Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5-32
45. Belgiu, M.; Draguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 2016, 114, 24-31
46. Gislason, P.O.; Benediktsson, J.A.; Sveinsson, J.R. Random Forests for land cover classification. Pattern Recognit. Lett. 2006, 27, 294-300
47. Pelletier, C.; Valero, S.; Inglada, J.; Champion, N.; Dedieu, G. Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas. Remote Sens. Environ. 2016, 187, 156-168
48. Koch, B. Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J Photogramm. Remote Sens. 2010, 65, 581-590
49. Wang, L.; Zhou, X.; Zhu, X.; Dong, Z.; Guo, W. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. Crop J. 2016, 4, 212-219
50. Liu, Y.; Cheng, T.; Zhu, Y.; Tian, Y.; Cao, W.; Yao, X.; Wang, N. Comparative analysis of vegetation indices, non-parametric and physical retrieval methods for monitoring nitrogen in wheat using UAV-based multispectral imagery. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, 10-15 July 2016; pp. 7362-7365
51. Yue, J.; Feng, H.; Yang, G.; Li, Z. A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy. Remote Sens. 2018, 10
52. Clevers, J.G.P.W.; Kooistra, L. Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 574-583
53. Schlemmer, M.; Gitelson, A.; Schepers, J.; Ferguson, R.; Peng, Y.; Shanahan, J.; Rundquist, D. Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. Int. J. Appl. Earth Obs. Geoinf. 2013, 25, 47-54
54. Portz, G.; Gnyp, M.L.; Jasper, J. Capability of crop canopy sensing to predict crop parameters of cut grass swards aiming at early season variable rate nitrogen top dressings. Adv. Anim. Biosci. 2017, 8, 792-795
55. Näsi, R.; Viljanen, N.; Kaivosoja, J.; Hakala, T.; Pandžić, M.; Markelin, L.; Honkavaara, E. Assessment of various remote sensing technologies in biomass and nitrogen content estimation using an agricultural test field. ISPRS Int. Archm. Photogramm. Remote Sens. Spat. Inform. Sci. 2017, XLII-3/W3, 137-141
56. Häkli, P.; Box, P.O. Practical Test on Accuracy and Usability of Virtual Reference Station Method in Finland; FIGWorkingWeek: Athens, Greece, 2004
57. Honkavaara, E.; Markelin, L.; Hakala, T.; Peltoniemi, J. The Metrology of Directional, Spectral Reflectance Factor Measurements Based on Area Format Imaging by UAVs. Available online: http://www. ingentaconnect.com/content/schweiz/pfg/2014/00002014/00000003/art00002 (accessed on 23 May 2018)
58. Hakala, T.; Markelin, L.; Honkavaara, E.; Scott, B.; Theocharous, T.; Nevalainen, O.; Näsi, R.; Suomalainen, J.; Viljanen, N.; Greenwell, C.; et al. Direct Reflectance Measurements from Drones: Sensor Absolute Radiometric Calibration and System Tests for Forest Reflectance Characterization. Sensors 2018, 18
59. National Land Survey of Finland. Finnref GNSS RINEX Service. 2018. Available online: https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/positioning-services/rinex-palvelu (accessed on 28 May 2018)
60. Honkavaara, E.; Rosnell, T.; Oliveira, R.; Tommaselli, A. Band registration of tuneable frame format hyperspectral UAV imagers in complex scenes. ISPRS J. Photogramm. Remote Sens. 2017, 134, 96-109
61. Cunliffe, A.M.; Brazier, R.E.; Anderson, K. Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sens. Environ. 2016, 183, 129-143
62. Méndez-Barroso, L.A.; Zárate-Valdez, J.L.; Robles-Morúa, A. Estimation of hydromorphological attributes of a small forested catchment by applying the Structure from Motion (SfM) approach. Int. J. Appl. Earth Obs. Geoinf. 2018, 69, 186-197
63. Honkavaara, E.; Khoramshahi, E. Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment. Remote Sens. 2018, 10, 256
64. Smith, G.M.; Milton, E.J. The use of the empirical line method to calibrate remotely sensed data to reflectance. Int. J. Remote Sens. 1999, 20, 2653-2662
65. Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127-150
66. Woebbecke, D.M.; Meyer, G.E.; Von Bargen, K.; Mortensen, D.A. Color Indices forWeed Identification Under Various Soil, Residue, and Lighting Conditions. Trans. ASAE 1995, 38, 259-269
67. Roujean, J.-L.; Breon, F.-M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 1995, 51, 375-384
68. Rouse, J.W. Monitoring Vegetation Systems in the Great Plains with ERTS; NASA: Washington, DC, USA, 1974
69. Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 1996, 55, 95-107
70. Guyot, G.; Baret, F. Utilisation de la haute resolution spectrale pour suivre l'etat des couverts vegetaux. Spectr. Signat. Objects Remote Sens. 1988, 287, 279
71. Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289-298
72. Daughtry, C.S.T.; Walthall, C.L.; Kim, M.S.; de Colstoun, E.B.; McMurtrey, J.E. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sens. Environ. 2000, 74, 229-239
73. Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 2004, 90, 337-352
74. Dash, J.; Curran, P.J. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 2004, 25, 5403-5413
75. Gitelson, A.A.; Gritz, Y.; Merzlyak, M.N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 2003, 160, 271-282
76. Hernández-Clemente, R.; Navarro-Cerrillo, R.M.; Suárez, L.;Morales, F.; Zarco-Tejada, P.J. Assessing structural effects on PRI for stress detection in conifer forests. Remote Sens. Environ. 2011, 115, 2360-2375
77. Miyoshi, G.T.; Imai, N.N.; Tommaselli, A.M.G.; Honkavaara, E.; Näsi, R.; Moriya, é.A.S. Radiometric block adjustment of hyperspectral image blocks in the Brazilian environment. Int. J. Remote Sens. 2018, 1-21
78. Danner, M.; Berger, K.; Wocher, M.; Mauser, W.; Hank, T. Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy. Remote Sens. 2017, 9
79. Roosjen, P.P.J.; Brede, B.; Suomalainen, J.M.; Bartholomeus, H.M.; Kooistra, L.; Clevers, J.G.P.W. Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data-Potential of unmanned aerial vehicle imagery. Int. J. Appl. Earth Obs. Geoinf. 2018, 66, 14-26
80. Näsi, R.; Viljanen, N.; Oliveira, R.; Kaivosoja, J.; Niemeläinen, O.; Hakala, T.; Markelin, L.; Nezami, S.; Suomalainen, J.; Honkavaara, E. Optimizing radiometric processing and feature extraction of drone based hyperspectral frame format imagery for estimation of yield quantity and quality of a grass sward. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci. 2018, XLII-3, 1305-1310
81. Zhang, L.; Zhang, L.; Du, B. Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art. IEEE Geosci. Remote Sens. Mag. 2016, 4, 22-40