Object-based image classification of summer crops with machine learning methods

Remote Sensing

Volumn 6, Issue 6, 2014, Pages 5019-5041

  Peña, José M ^a,b   Gutiérrez, Pedro A ^c   Hervás Martínez, César ^c   Six, Johan ^d   Plant, Richard E ^b   López Granados, Francisca ^a  

^a INSTITUTO DE AGRICULTURA SOSTENIBLE   (Spain)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CÓRDOBA   (Spain)

^d ETH ZURICH   (Switzerland)

Author keywords

Agriculture; ASTER satellite images; Hierarchical classification; Neural networks; Object oriented image analysis

Indexed keywords

AGRICULTURAL MACHINERY; AGRICULTURE; ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPLEX NETWORKS; CROPS; DECISION TREES; IMAGE ANALYSIS; IMAGE SEGMENTATION; LEARNING SYSTEMS; NEURAL NETWORKS; REMOTE SENSING; SUPPORT VECTOR MACHINES;

DECISION TREE CLASSIFIERS; HIERARCHICAL CLASSIFICATION; HIERARCHICAL CLASSIFIERS; MULTILAYER PERCEPTRON NEURAL NETWORKS; OBJECT BASED IMAGE ANALYSIS; OBJECT-BASED IMAGE CLASSIFICATION; OBJECT-ORIENTED IMAGE ANALYSIS; SATELLITE IMAGES;

IMAGE CLASSIFICATION;

EID: 84908572688     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs6065019     Document Type: Article

References (46)

1. Pacala, S.; Socolow, R. Stabilization wedges: Solving the climate problem for the next 50 years with current technologies. Science 2004, 305, 968-972.
2. Peña-Barragán, J.M.; López-Granados, F.; García Torres, L.; Jurado-Expósito, M.; Sánchez-de la Orden, M.; García-Ferrer, A. Discriminating cropping systems and agro-environmental measures by remote sensing. Agron. Sustain. Dev. 2008, 28, 355-362.
3. De Wit, A.J.W.; Clevers, J.G.P.W. Efficiency and accuracy of per-field classification for operational crop mapping. Int. J. Remote Sens. 2004, 25, 4091-4112.
4. Pan, Y.; Hu, T.; Zhu, X.; Zhang, J.; Wang, X. Mapping cropland distributions using a hard and soft classification model. IEEE Trans. Geosci. Remote Sens. 2012, 50, 4301-4312.
5. Peña-Barragán, J.M.; Ngugi, M.K.; Plant, R.E.; Six, J. Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sens. Environ. 2011, 115, 1301-1316.
6. Serra, P.; Pons, X. Monitoring farmers' decisions on Mediterranean irrigated crops using satellite image time series. Int. J. Remote Sens. 2008, 29, 2293-2316.
7. 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. 2008, 29, 95-116.
8. Castillejo-González, I.L.; López-Granados, F.; García-Ferrer, A.; Peña-Barragán, J.M.; Jurado-Expósito, M.; de la Orden, M.S.; González-Audicana, M. Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery. Comput. Electron. Agric. 2009, 68, 207-215.
9. Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 2010, 65, 2-16.
10. Lu, D.; Mausel, P.; Batistella, M.; Moran, E. Comparison of land-cover classification methods in the Brazilian Amazon Basin. Photogramm. Eng. Remote Sens. 2004, 70, 723-731.
11. Cherkassky, V.S.; Mulier, F. Learning from Data Concepts, Theory, and Methods; Wiley-Interscience: Hoboken, NJ, USA, 2007.
12. Kocev, D.; Vens, C.; Struyf, J.; Džeroski, S. Tree ensembles for predicting structured outputs. Pattern Recognit. 2013, 46, 817-833.
13. Silla, C.; Freitas, A. A survey of hierarchical classification across different application domains. Data Min. Knowl. Discov. 2011, 22, 31-72.
14. Kimes, D.S.; Nelson, R.F.; Manry, M.; Fung, A.K. Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements. Int. J. Remote Sens. 1998, 19, 2639-2663.
15. Foody, G.M. Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes. Int. J. Remote Sens. 2004, 25, 3091-3104.
16. Duro, D.C.; Franklin, S.E.; Dubé, M.G. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ. 2012, 118, 259-272.
17. Gutiérrez, P.A.; López-Granados, F.; Peña-Barragán, J.M.; Jurado-Expósito, M.; Hervás-Martínez, C. Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data. Comput. Electron. Agric. 2008, 64, 293-306.
18. Pontius, R.G.; Millones, M. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 2011, 32, 4407-4429.
19. Yolo County Agriculture Department. Yolo County: Agricultural Crop Report 2006; County of Yolo: Woodland, CA, USA, 2006.
20. Lopez-Granados, F.; Gomez-Casero, M.T.; Peña-Barragan, J.M.; Jurado-Exposito, M.; Garcia-Torres, L. Classifying irrigated crops as affected by phenological stage using Discriminant analysis and neural networks. J. Am. Soc. Hort. Sci. 2010, 135, 465-473.
21. Matthew, M.W.; Adler-Golden, S.M.; Berk, A.; Richtsmeier, S.C.; Levine, R.Y.; Bernstein, L.S.; Acharya, P.K.; Anderson, G.P.; Felde, G.W.; Hoke, M.L.; et al. Status of atmospheric correction using a MODTRAN4-based algorithm. Proc. SPIE 2000, 4049, 199-207.
22. Hatfield, J.L.; Prueger, J.H. Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management Practices. Remote Sens. 2010, 2, 562-578.
23. Gitelson, A.A.; Kaufman, Y.J.; Stark, R.; Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 2002, 80, 76-87.
24. 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.
25. Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS. In NASA Sp-351 I; NASA: Washington, DC, USA, 12/1973; pp. 309-317.
26. McNairn, H.; Protz, R. Mapping corn residue cover on agricultural fields in Oxford County, Ontario, using thematic mapper. Can. J. Remote Sens. 1993, 19, 152-159.
27. Qi, J.; Marsett, R.C.; Heilman, P.; Biedenbender, S.; Moran, M.S.; Goodrich, D.C. RANGES improves satellite-based information and land cover assessments in southwest United States. Eos Trans. Am. Geophys. Union 2002, 83, 601-606.
28. Van Deventer, A.P.; Ward, A.P.; Gowda, P.H.; Lyon, J.G. Using Thematic Mapper data to identify contrasting soil plains to tillage practices. Photogramm. Eng. Remote Sens. 1997, 63, 87-93.
29. Daughtry, C.S.T.; Hunt, E.R.; Doraiswamy, P.C.; McMurtrey, J.E. remote sensing the spatial distribution of crop residues. Agron. J. 2005, 97, 864-871.
30. Serbin, G.; Hunt, E.R., Jr.; Daughtry, C.S.T.; McCarty, G.W.; Doraiswamy, P.C. An improved ASTER index for remote sensing of crop residue. Remote Sens. 2009, 1, 971-991.
31. Trimble Germany GmbH. eCognition Developer 8.7.2. Reference book; Trimble Germany GmbH: Munich, Germany, 2013.
32. Ortiz, A.; Oliver, G. On the use of the overlapping area matrix for image segmentation evaluation: A survey and new performance measures. Pattern Recognit. Lett. 2006, 27, 1916-1926.
33. Dorigo, W.; Lucieer, A.; Podobnikar, T.; Carni, A. Mapping invasive Fallopia japonica by combined spectral, spatial, and temporal analysis of digital orthophotos. Int. J. Appl. Earth Obs. Geoinformation 2012, 19, 185-195.
34. Haralick, R.M.; Shanmugam, K.; Dinstein, I. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 1973, 3, 610-621.
35. Baraldi, A.; Parmiggiani, F. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Trans. Geosci. Remote Sens. 1995, 33, 293-304.
36. Mather, P.M.; Koch, M. Computer Processing of Remotely-Sensed Images: An Introduction, 4th ed.; Wiley-Blackwell: Chichester, UK, 2011.
37. Witten, I.H.; Frank, E. Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed.; Morgan Kaufmann Publishers: San Francisco, CA, USA, 2005.
38. Friedman, J.; Hastie, T.; Tibshirani, R. Additive logistic regression: A statistical view of boosting. Ann. Stat. 2000, 28, 337-407.
39. Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20, 273-297.
40. Quinlan, J.R. C4.5: Programs for Machine Learning; Morgan Kaufmann: San Mateo, CA, USA, 1993.
41. Gutiérrez, P.A.; Hervás-Martínez, C.; Martínez-Estudillo, F.J.; Carbonero, M. A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems. Inf. Sci. 2012, 197, 20-37.
42. Friedl, M.A.; Brodley, C.E. Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 1997, 61, 399-409.
43. Elnaggar, A.A.; Noller, J.S. Application of remote-sensing data and decision-tree analysis to mapping salt-affected soils over large areas. Remote Sens. 2009, 2, 151-165.
44. Hollander, M.; Wolfe, D.A.; Chicken, E. Nonparametric Statistical Methods, 3rd ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2014.
45. Steele, B.M. Combining multiple classifiers: An application using spatial and remotely sensed information for land cover type mapping. Remote Sens. Environ. 2000, 74, 545-556.
46. Conrad, C.; Fritsch, S.; Zeidler, J.; Rücker, G.; Dech, S. Per-field irrigated crop classification in arid central Asia using SPOT and ASTER data. Remote Sens. 2010, 2, 1035-1056.