Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system

GIScience and Remote Sensing

Volumn 55, Issue 2, 2018, Pages 243-264

  Liu, Tao ^a   Abd Elrahman, Amr ^a   Morton, Jon ^b   Wilhelm, Victor L ^c  

^a UNIVERSITY OF FLORIDA   (United States)

^b U S ARMY ENGINEER RESEARCH AND DEVELOPMENT CENTER   (United States)

^c US Army Corps of Engineers Jacksonville District   (United States)

Author keywords

convolutional neural network; deep learning; FCN; OBIA; UAS

Indexed keywords

ACCURACY ASSESSMENT; AIRCRAFT; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; IMAGE ANALYSIS; LAND COVER; SUPPORT VECTOR MACHINE; UNMANNED VEHICLE; VEGETATION MAPPING; WETLAND;

EID: 85040978796     PISSN: 15481603     EISSN: None     Source Type: Journal    
DOI: 10.1080/15481603.2018.1426091     Document Type: Article

References (98)

1. Ajaz Ahmed, M. A., A., Abd-Elrahman, F. J., Escobedo, W. P., Cropper Jr, T. A., Martin, and N., Timilsina. 2017. “Spatially-Explicit Modeling of Multi-Scale Drivers of Aboveground Forest Biomass and Water Yield in Watersheds of the Southeastern United States.” 199: 158.
2. Alshehhi, R., P. R., Marpu, W. L., Woon, and M., Dalla Mura. 2017. “Simultaneous Extraction of Roads and Buildings in Remote Sensing Imagery with Convolutional Neural Networks.” ISPRS Journal of Photogrammetry and Remote Sensing 130: 139–149. doi:10.1016/j.isprsjprs.2017.05.002.
3. Ahmed, Mukhtar, Ahmed, Ajaz, et al. “Spatially-explicit modelingof multi-scale drivers of aboveground forest biomass and water yield in watersheds of the Southeastern United States”. Journal of Environmental Management 199 (2017): 158–171.
4. Arthington, J., F., Roka, J., Mullahey, S., Coleman, R., Muchovej, L., Lollis, and D., Hitchcock. 2007. “Integrating Ranch Forage Production, Cattle Performance, and Economics in Ranch Management Systems for Southern Florida.” Rangeland Ecology & Management 60: 12–18. doi:10.2111/05-074R1.1.
5. Belgiu, M., and L., Drăguţ. 2016. “Random Forest in Remote Sensing: A Review of Applications and Future Directions.” ISPRS Journal of Photogrammetry and Remote Sensing 114: 24–31. doi:10.1016/j.isprsjprs.2016.01.011.
6. Blaschke, T., 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing 65: 2–16. doi:10.1016/j.isprsjprs.2009.06.004.
7. Breiman, L., 2001. “Random Forests.” Machine Learning 45: 5-32. doi:10.1023/A:1010933404324.
8. Camacho-de Coca, F., F. M., Bréon, M., Leroy, and F. J., Garcia-Haro. 2004. “Airborne Measurement of Hot Spot Reflectance Signatures.” Remote Sensing of Environment 90: 63–75. doi:10.1016/j.rse.2003.11.019.
9. Cheng, G., P., Zhou, and J., Han. 2016. “Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 54: 7405–7415. doi:10.1109/TGRS.2016.2601622.
10. Cortes, C., and V., Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20: 273–297. doi:10.1007/BF00994018.
11. Davis, S., and J. C., Ogden. 1994. Everglades: The Ecosystem and Its Restoration. CRC Press, Florida, Boca Raton.
12. Developer, E., 2012. Trimble eCognition Developer 8.7 Reference Book. Trimble Germany GmbH, Munich.
13. Díaz-Uriarte, R., and S. A., De Andres. 2006. “Gene Selection and Classification of Microarray Data Using Random Forest.” BMC Bioinformatics 7: 3. doi:10.1186/1471-2105-7-3.
14. Drăguţ, L., O., Csillik, C., Eisank, and D., Tiede. 2014. “Automated Parameterisation for Multi-Scale Image Segmentation on Multiple Layers.” ISPRS Journal of Photogrammetry and Remote Sensing 88: 119–127. doi:10.1016/j.isprsjprs.2013.11.018.
15. eCognition, 2012. Features Reference, http://community.ecognition.com/home/features-reference.
16. Fernández, T., J. L., Pérez, J., Cardenal, J. M., Gómez, C., Colomo, and J., Delgado. 2016. “Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques.” Remote Sensing 8: 837. doi:10.3390/rs8100837.
17. Ferriter, A., K., Serbesoff-King, M., Bodle, C., Goodyear, B., Doren, and K., Langeland. 2004. “Exotic Species in the Everglades Protection Area.” In: 2004 Everglades Consolidated Report, 11–15. South FloridaWater Management District (SFWMD) and Florida Department ofEnvironmental Protection (FDEP).
18. Gleason, C. J., and J., Im. 2012. “Forest Biomass Estimation from Airborne LiDAR Data Using Machine Learning Approaches.” Remote Sensing of Environment 125: 80–91. doi:10.1016/j.rse.2012.07.006.
19. Grybas, H., L., Melendy, and R. G., Congalton. 2017. “A Comparison of Unsupervised Segmentation Parameter Optimization Approaches Using Moderate- and High-Resolution Imagery.” GIScience & Remote Sensing,54: 1–19.
20. He, K., X., Zhang, S., Ren, and J., Sun. 2016. “Deep Residual Learning for Image Recognition.” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 770–778.
21. Hinton, G., L., Deng, D., Yu, G. E., Dahl, A.-R., Mohamed, N., Jaitly, A., Senior, V., Vanhoucke, P., Nguyen, and T. N., Sainath. 2012. “Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups.” IEEE Signal Processing Magazine 29: 82–97. doi:10.1109/MSP.2012.2205597.
22. Hinton, G. E., S., Osindero, and Y.-W., Teh. 2006. “A Fast Learning Algorithm for Deep Belief Nets.” Neural Computation 18: 1527–1554. doi:10.1162/neco.2006.18.7.1527.
23. Holm, L. G., D. L., Plucknett, J. V., Pancho, and J. P., Herberger. 1977. The World’s Worst Weeds. University Press, Honolulu, Hawaii.
24. Hsu, C.-W., and C.-J., Lin. 2002. “A Comparison of Methods for Multiclass Support Vector Machines.” IEEE Transactions on Neural Networks 13: 415–425. doi:10.1109/72.991427.
25. Huval, B., T., Wang, S., Tandon, J., Kiske, W., Song, J., Pazhayampallil, M., Andriluka, P., Rajpurkar, T., Migimatsu, and R., Cheng-Yue. 2015. “An Empirical Evaluation of Deep Learning on Highway Driving.” arXiv Preprint arXiv:1504.01716.
26. Hu, W., Huang, Y., Wei, L., Zhang, F., Li, H., 2015. Deepconvolutional neural networks for hyperspectral image classification. Journal of Sensors.
27. Im, J., J., Jensen, and J., Tullis. 2008. “Object‐Based Change Detection Using Correlation Image Analysis and Image Segmentation.” International Journal of Remote Sensing 29: 399–423. doi:10.1080/01431160601075582.
28. Im, J., S., Park, J., Rhee, J., Baik, and M., Choi. 2016. “Downscaling of AMSR-E Soil Moisture with MODIS Products Using Machine Learning Approaches.” Environmental Earth Sciences 75: 1120. doi:10.1007/s12665-016-5917-6.
29. Ioffe, S., and C., Szegedy. 2015. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.” arXiv Preprint arXiv:1502.03167.
30. Jensen, J. R., K., Rutchey, M. S., Koch, and S., Narumalani. 1995. “Inland Wetland Change Detection in the Everglades Water Conservation Area 2A Using a Time Series of Normalized Remotely Sensed Data.” Photogrammetric Engineering and Remote Sensing 61: 199–209.
31. Ke, Y., L. J., Quackenbush, and J., Im. 2010. “Synergistic Use of QuickBird Multispectral Imagery and LIDAR Data for Object-Based Forest Species Classification.” Remote Sensing of Environment 114: 1141–1154. doi:10.1016/j.rse.2010.01.002.
32. Keddy, P. A., 2010. Wetland Ecology: Principles and Conservation. Cambridge University Press: NY, New York City.
33. Koukal, T., and C., Atzberger. 2012. “Potential of Multi-Angular Data Derived from a Digital Aerial Frame Camera for Forest Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5: 30–43. doi:10.1109/JSTARS.2012.2184527.
34. Krizhevsky, A., I., Sutskever, and G. E., Hinton. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” Advances in Neural Information Processing Systems 1097–1105.
35. Kussul, N., M., Lavreniuk, S., Skakun, and A., Shelestov. 2017. “Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data.” IEEE Geoscience and Remote Sensing Letters 14: 778–782. doi:10.1109/LGRS.2017.2681128.
36. LeCun, Y., Y., Bengio, and G., Hinton. 2015. “Deep Learning.” Nature 521: 436–444. doi:10.1038/nature14539.
37. Lee, S., H., Han, J., Im, E., Jang, and M.-I., Lee. 2017. “Detection of Deterministic and Probabilistic Convection Initiation Using Himawari-8 Advanced Himawari Imager Data.” Atmospheric Measurement Techniques 10: 1859–1874. doi:10.5194/amt-10-1859-2017.
38. Li, L., Y., Chen, T., Xu, R., Liu, K., Shi, and C., Huang. 2015. “Super-Resolution Mapping of Wetland Inundation from Remote Sensing Imagery Based on Integration of Back-Propagation Neural Network and Genetic Algorithm.” Remote Sensing of Environment 164: 142–154. doi:10.1016/j.rse.2015.04.009.
39. Li, M., J., Im, L. J., Quackenbush, and T., Liu. 2014. “Forest Biomass and Carbon Stock Quantification Using Airborne LiDAR Data: A Case Study over Huntington Wildlife Forest in the Adirondack Park.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7: 3143–3156. doi:10.1109/JSTARS.2014.2304642.
40. Li, M., L., Ma, T., Blaschke, L., Cheng, and D., Tiede. 2016a. “A Systematic Comparison of Different Object-Based Classification Techniques Using High Spatial Resolution Imagery in Agricultural Environments.” International Journal of Applied Earth Observation and Geoinformation 49: 87–98. doi:10.1016/j.jag.2016.01.011.
41. Li, Z., W., Shi, P., Lu, L., Yan, Q., Wang, and Z., Miao. 2016b. “Landslide Mapping from Aerial Photographs Using Change Detection-Based Markov Random Field.” Remote Sensing of Environment 187: 76–90. doi:10.1016/j.rse.2016.10.008.
42. Liu, M., Wang, M., Wang, J., Li, D., 2013. Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the Page 2 of 4 recognition of orange beverage and Chinese vinegar. Sensors and Actuators B: Chemical 177, 970–980.
43. Liu, T., J., Im, and L. J., Quackenbush. 2015. “A Novel Transferable Individual Tree Crown Delineation Model Based on Fishing Net Dragging and Boundary Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 110: 34–47. doi:10.1016/j.isprsjprs.2015.10.002.
44. Long, J., E., Shelhamer, and T., Darrell. 2015. “Fully Convolutional Networks for Semantic Segmentation.” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 3431–3440.
45. Lu, B., and Y., He. 2017. “Species Classification Using Unmanned Aerial Vehicle (UAV)-Acquired High Spatial Resolution Imagery in a Heterogeneous Grassland.” ISPRS Journal of Photogrammetry and Remote Sensing 128: 73–85. doi:10.1016/j.isprsjprs.2017.03.011.
46. Ma, L., M., Li, X., Ma, L., Cheng, P., Du, and Y., Liu. 2017. “A Review of Supervised Object-Based Land-Cover Image Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 130: 277–293. doi:10.1016/j.isprsjprs.2017.06.001.
47. Ma, X., H., Wang, and J., Wang. 2016. “Semisupervised Classification for Hyperspectral Image Based on Multi-Decision Labeling and Deep Feature Learning.” ISPRS Journal of Photogrammetry and Remote Sensing 120: 99–107. doi:10.1016/j.isprsjprs.2016.09.001.
48. Makantasis, K., K., Karantzalos, A., Doulamis, and N., Doulamis. 2015. “Deep Supervised Learning for Hyperspectral Data Classification through Convolutional Neural Networks.” In: Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International, 4959–4962. IEEE.
49. Marmanis, D., K., Schindler, J.D., Wegner, S., Galliani, and M, Datcu.,Stilla, U., 2016. Classification with an edge: improving semanticimage segmentation with boundary detection. arXiv preprintarXiv: 1612.01337.
50. Martínez-López, J., M. F., Carreño, J. A., Palazón-Ferrando, J., Martínez-Fernández, and M. A., Esteve. 2014. “Remote Sensing of Plant Communities as a Tool for Assessing the Condition of Semiarid Mediterranean Saline Wetlands in Agricultural Catchments.” International Journal of Applied Earth Observation and Geoinformation 26: 193–204. doi:10.1016/j.jag.2013.07.005.
51. McGwire, K. C., M. A., Weltz, J. A., Finzel, C. E., Morris, L. F., Fenstermaker, and D. S., McGraw. 2013. “Multiscale Assessment of Green Leaf Cover in a Semi-Arid Rangeland with a Small Unmanned Aerial Vehicle.” International Journal of Remote Sensing 34: 1615–1632. doi:10.1080/01431161.2012.723836.
52. Mitsch, W. J., and J. G., Gosselink. 2015. Wetlands. 5th ed. Hoboken, NJ: Wiley.
53. Murakami, T., M., Yui, and K., Amaha. 2012. “Canopy Height Measurement by Photogrammetric Analysis of Aerial Images: Application to Buckwheat (Fagopyrum Esculentum Moench) Lodging Evaluation.” Computers and Electronics in Agriculture 89: 70–75. doi:10.1016/j.compag.2012.08.003.
54. Nair, V., and G. E., Hinton. 2010. “Rectified Linear Units Improve Restricted Boltzmann Machines.” In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), 807–814.
55. Pajares, G., 2015. “Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs).” Photogrammetric Engineering & Remote Sensing 81: 281–330. doi:10.14358/PERS.81.4.281.
56. Pal, M., 2005. “Random Forest Classifier for Remote Sensing Classification.” International Journal of Remote Sensing 26: 217–222. doi:10.1080/01431160412331269698.
57. Pande-Chhetri, R., A., Abd-Elrahman, T., Liu, J., Morton, and V. L., Wilhelm. 2017. “Object-Based Classification of Wetland Vegetation Using Very High-Resolution Unmanned Air System Imagery.” European Journal of Remote Sensing 50: 564–576. doi:10.1080/22797254.2017.1373602.
58. Park, S., J., Im, E., Jang, and J., Rhee. 2016. “Drought Assessment and Monitoring through Blending of Multi-Sensor Indices Using Machine Learning Approaches for Different Climate Regions.” Agricultural and Forest Meteorology 216: 157–169. doi:10.1016/j.agrformet.2015.10.011.
59. Pastor, E., C., Barrado, P., Royo, E., Santamaria, J., Lopez, and E., Salami. 2011. “Architecture for a Helicopter-Based Unmanned Aerial Systems Wildfire Surveillance System.” Geocarto International 26: 113–131. doi:10.1080/10106049.2010.531769.
60. Peppa, M., J., Mills, P., Moore, P., Miller, and J., Chambers. 2016. “Accuracy Assessment of a UAV-Based Landslide Monitoring System. ISPRS-International Archives of the Photogrammetry.” Remote Sensing and Spatial Information Sciences 895–902.
61. Primicerio, J., S. F., Di Gennaro, E., Fiorillo, L., Genesio, E., Lugato, A., Matese, and F. P., Vaccari. 2012. “A Flexible Unmanned Aerial Vehicle for Precision Agriculture.” Precision Agriculture 13: 517–523. doi:10.1007/s11119-012-9257-6.
62. Piramanayagam, S., Schwartzkopf, W., Koehler, F., Saber E., 2016. Classification of remote sensed images using random forests and deep learning framework, SPIE Remote Sensing. International Society for Optics and Photonics, pp. 100040L-100040L-100048.
63. Rango, A., A., Laliberte, C., Steele, J. E., Herrick, B., Bestelmeyer, T., Schmugge, A., Roanhorse, and V., Jenkins. 2006. “Using Unmanned Aerial Vehicles for Rangelands: Current Applications and Future Potentials.” Environmental Practice 8: 159–168. doi:10.1017/S1466046606060224.
64. Rhee, J., and J., Im. 2017. “Meteorological Drought Forecasting for Ungauged Areas Based on Machine Learning: Using Long-Range Climate Forecast and Remote Sensing Data.” Agricultural and Forest Meteorology 237: 105–122. doi:10.1016/j.agrformet.2017.02.011.
65. Rutchey, K., T., Schall, R., Doren, A., Atkinson, M., Ross, D., Jones, M., Madden, L., Vilchek, K., Bradley, and J., Snyder. 2006. Vegetation Classification for South Florida Natural Areas. Petersburg, FL: US Geological Survey St.
66. Saito, G., H., Seki, K., Uto, Y., Kosugi, and T., Komatsu. 2014. “Development of Hyperspectral Imaging Sensor, Which Mounted on UAV for Environmental Study at Coastal Zone.” In: 35th Asian Conference on Remote Sensing.
67. Samseemoung, G., P., Soni, H. P., Jayasuriya, and V. M., Salokhe. 2012. “Application of Low Altitude Remote Sensing (LARS) Platform for Monitoring Crop Growth and Weed Infestation in a Soybean Plantation.” Precision Agriculture 13: 611–627. doi:10.1007/s11119-012-9271-8.
68. Scherer, D., A., Müller, and S., Behnke. 2010. “Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition.” Artificial Neural Networks–ICANN 2010: 92–101.
69. Scholkopf, B., and A.J, Smola., 2001. Learning with kernels: supportvector machines, regularization, optimization, and beyond. MIT press.
70. Sheikh, P. A., 2002. “Florida Everglades Restoration: Background on Implementation and Early Lessons.” In: The Libraryof Congress: Congressional Research Service.
71. Sherrah, J. 2016. “Fully Convolutional Networks For Densesemantic Labelling Of High-resolution Aerial Imagery.” Arxiv Preprint arxiv 1606: 02585.
72. Silver, D., A., Huang, C. J., Maddison, A., Guez, L., Sifre, G., Van Den Driessche, J., Schrittwieser, I., Antonoglou, V., Panneershelvam, and M., Lanctot. 2016. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529: 484–489. doi:10.1038/nature16961.
73. Simonyan, K., and A., Zisserman. 2014. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” arXiv Preprint arXiv:1409.1556.
74. Skeele, R. C., and G. A., Hollinger. 2016. Aerial Vehicle Path Planning for Monitoring Wildfire Frontiers, Field and Service Robotics, 455–467. Springer.
75. Srivastava, N., G. E., Hinton, A., Krizhevsky, I., Sutskever, and R., Salakhutdinov. 2014. “Dropout: A Simple Way to Prevent Neural Networks from Overfitting.” Journal of Machine Learning Research 15: 1929–1958.
76. Statnikov, A., L., Wang, and C. F., Aliferis. 2008. “A Comprehensive Comparison of Random Forests and Support Vector Machines for Microarray-Based Cancer Classification.” BMC Bioinformatics 9: 319. doi:10.1186/1471-2105-9-319.
77. Suk, H.-I., S.-W., Lee, D., Shen, and A. S. D. N., Initiative. 2014. “Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis.” NeuroImage 101: 569–582. doi:10.1016/j.neuroimage.2014.06.077.
78. Suzuki, T., Y., Amano, T., Hashizume, S., Suzuki, and A., Yamaba. 2010. “Generation of Large Mosaic Images for Vegetation Monitoring Using a Small Unmanned Aerial Vehicle.” Journal of Robotics and Mechatronics 22: 212–220. doi:10.20965/jrm.2010.p0212.
79. Swain, H. M., E. H., Boughton, P. J., Bohlen, and L. O. G., Lollis. 2013. “Trade-Offs among Ecosystem Services and Disservices on a Florida Ranch.” Rangelands 35: 75–87. doi:10.2111/RANGELANDS-D-13-00053.1.
80. Szantoi, Z., F., Escobedo, A., Abd-Elrahman, S., Smith, and L., Pearlstine. 2013. “Analyzing Fine-Scale Wetland Composition Using High Resolution Imagery and Texture Features.” International Journal of Applied Earth Observation and Geoinformation 23: 204–212. doi:10.1016/j.jag.2013.01.003.
81. Vedaldi, A., and K., Lenc. 2015. “Matconvnet: Convolutional Neural Networks for Matlab.” In: Proceedings of the 23rd ACM International Conference on Multimedia, 689–692. ACM.
82. Vetrivel, A., M., Gerke, N., Kerle, F., Nex, and G., Vosselman. 2017. “Disaster Damage Detection through Synergistic Use of Deep Learning and 3D Point Cloud Features Derived from Very High Resolution Oblique Aerial Images, and Multiple-Kernel-Learning.” ISPRS Journal of Photogrammetry and Remote Sensing. doi:10.1016/j.isprsjprs.2017.03.001.
83. Wang, C., R. T., Pavlowsky, Q., Huang, and C., Chang. 2016. “Channel Bar Feature Extraction for a Mining-Contaminated River Using High-Spatial Multispectral Remote-Sensing Imagery.” GIScience & Remote Sensing 53: 283–302. doi:10.1080/15481603.2016.1148229.
84. Wright, C., and A., Gallant. 2007. “Improved Wetland Remote Sensing in Yellowstone National Park Using Classification Trees to Combine TM Imagery and Ancillary Environmental Data.” Remote Sensing of Environment 107: 582–605. doi:10.1016/j.rse.2006.10.019.
85. Xie, M., N., Jean, M., Burke, D., Lobell, and S., Ermon. 2015. “Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping.” arXiv Preprint arXiv:1510.00098.
86. Yegnanarayana, B., 2009. Artificial neural networks. PHI Learning Pvt. Ltd
87. Yu, Q., P., Gong, N., Clinton, G., Biging, M., Kelly, and D., Schirokauer. 2006. “Object-Based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery.” Photogrammetric Engineering & Remote Sensing 72: 799–811. doi:10.14358/PERS.72.7.799.
88. Yue, J., T., Lei, C., Li, and J., Zhu. 2012. “The Application of Unmanned Aerial Vehicle Remote Sensing in Quickly Monitoring Crop Pests.” Intelligent Automation & Soft Computing 18: 1043–1052. doi:10.1080/10798587.2008.10643309.
89. Yunxia, H., L., Minzan, Z., Xijie, J., Liangliang, C., Xingping, and Z., Fusuo. 2005. “Precision Management of Winter Wheat Based on Aerial Images and Hyperspectral Data Obtained by Unmanned Aircraft.” In: Geoscience and Remote Sensing Symposium, 2005. IGARSS’05. Proceedings. 2005 IEEE International, 3109–3112. IEEE.
90. Zarco-Tejada, P. J., V., González-Dugo, and J. A., Berni. 2012. “Fluorescence, Temperature and Narrow-Band Indices Acquired from a UAV Platform for Water Stress Detection Using a Micro-Hyperspectral Imager and a Thermal Camera.” Remote Sensing of Environment 117: 322–337. doi:10.1016/j.rse.2011.10.007.
91. Zedler, J. B., and S., Kercher. 2004. “Causes and Consequences of Invasive Plants in Wetlands: Opportunities, Opportunists, and Outcomes.” Critical Reviews in Plant Sciences 23: 431–452. doi:10.1080/07352680490514673.
92. Zedler, J. B., and S., Kercher. 2005. “Wetland Resources: Status, Trends, Ecosystem Services, and Restorability.” Annu. Rev. Environ. Resour. 30: 39–74. doi:10.1146/annurev.energy.30.050504.144248.
93. Zeggada, A., F., Melgani, and Y., Bazi. 2017. “A Deep Learning Approach to UAV Image Multilabeling.” IEEE Geoscience and Remote Sensing Letters 14: 694–698. doi:10.1109/LGRS.2017.2671922.
94. Zhang, P., M., Gong, L., Su, J., Liu, and Z., Li. 2016. “Change Detection Based on Deep Feature Representation and Mapping Transformation for Multi-Spatial-Resolution Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 116: 24–41. doi:10.1016/j.isprsjprs.2016.02.013.
95. Zhao, W., and S., Du. 2016. “Learning Multiscale and Deep Representations for Classifying Remotely Sensed Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 113: 155–165. doi:10.1016/j.isprsjprs.2016.01.004.
96. Zhao, W., S., Du, and W. J., Emery. 2017. “Object-Based Convolutional Neural Network for High-Resolution Imagery Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10: 3386–3396. doi:10.1109/JSTARS.2017.2680324.
97. Zhong, P., Z., Gong, S., Li, and C.-B., Schönlieb. 2017. “Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing. doi:10.1109/TGRS.2017.2755542.
98. Zweig, C. L., M. A., Burgess, H. F., Percival, and W. M., Kitchens. 2015. “Use of Unmanned Aircraft Systems to Delineate Fine-Scale Wetland Vegetation Communities.” Wetlands 35: 303–309. doi:10.1007/s13157-014-0612-4.