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Articles

The application of discriminant analysis for mapping cereals and pasture using object-based features

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Pages 5546-5568 | Received 12 May 2015, Accepted 23 Apr 2017, Published online: 23 Jun 2017

References

  • Amato, U., A. Antoniadis, M. F. Carfora, P. Colandrea, V. Cuomo, M. Franzese, and C. Serio. 2013. “Statistical Classification for Assessing PRISMA Hyperspectral Potential for Agricultural Land Use.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (2): 615–625. doi:10.1109/JSTARS.2013.2255981.
  • 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.
  • Benz, U. C., P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen. 2004. “Multi- Resolution, Object-Oriented Fuzzy Analysis of Remote Sensing Data for GIS-Ready Information.” ISPRS Journal of Photogrammetry and Remote Sensing 58 (3–4): 239–258. doi:10.1016/j.isprsjprs.2003.10.002.
  • Blaschke, T. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 2–16. doi:10.1016/j.isprsjprs.2009.06.004.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
  • Byrne, G. F., P. F. Crapper, and K. K. Mayo. 1980. “Monitoring Land-Cover Change by Principal Component Analysis of Multitemporal Landsat Data.” Remote Sensing of Environment 10 (3): 175–184. doi:10.1016/0034-4257(80)90021-8.
  • Castillejo-González, I. L., F. López-Granados, A. García-Ferrer, J. M. Peña-Barragán, M. Jurado-Expósito, M. S. de la Orden, and M. González-Audicana. 2009. “Object-And Pixel-Based Analysis for Mapping Crops and Their Agro-Environmental Associated Measures Using Quickbird Imagery.” Computers and Electronics in Agriculture 68 (2): 207–215. doi:10.1016/j.compag.2009.06.004.
  • Champagne, C., A. Davidson, P. Cherneski, J. L’Heureux, and T. Hadwen. 2014. “Monitoring Agricultural Risk in Canada Using L-Band Passive Microwave Soil Moisture from SMOS.” Journal of Hydrometeorology 16: 5–18. doi:10.1175/JHM-D-14-0039.1.
  • Champagne, C., T. Rowlandson, A. Berg, T. Burns, J. L’Heureux, E. Tetlock, J. R. Adams, H. McNairn, B. Toth, and D. Itenfisu. 2016. “Satellite Surface Soil Moisture from SMOS and Aquarius: Assessment for Applications in Agricultural Landscapes.” International Journal of Applied Earth Observation and Geoinformation 45: 143–154. doi:10.1016/j.jag.2015.09.004.
  • Chini, M., F. Pacifici, W. J. Emery, N. Pierdicca, and F. D. Frate. 2008. “Comparing Statistical and Neural Network Methods Applied to Very High Resolution Satellite Images Showing Changes in Man-Made Structures at Rocky Flats.” IEEE Transactions on Geoscience and Remote Sensing 46 (6): 1812–1821. doi:10.1109/TGRS.2008.916223.
  • Congalton, R. G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37 (1): 35–46. doi:10.1016/0034-4257(91)90048-B.
  • Davidson, A. 2009. A National Crop Monitoring System Prototype (NCMS-P) Using MODIS Data: Near-Real-Time Agricultural Assessment from Space. Ottawa, ON: Agriculture and Agri-Food Canada (AAFC).
  • Deschamps, B., H. McNairn, J. Shang, and X. Jiao. 2012. “Towards Operational Radar-Only Crop Type Classification: Comparison of a Traditional Decision Tree with a Random Forest Classifier.” Canadian Journal of Remote Sensing 38 (1): 60–68. doi:10.5589/m12-012.
  • eCognition. 2013. Ecognition Developer Reference Book (Ver. 8.9.1). In. Arnulfstrasse 126, D-80636 Munich. Germany: Trimble Germany GmbH.
  • Filella, I., L. Serrano, J. Serra, and J. Penuelas. 1995. “Evaluating Wheat Nitrogen Status with Canopy Reflectance Indices and Discriminant Analysis.” Crop Science 35 (5): 1400–1405. doi:10.2135/cropsci1995.0011183X003500050023x.
  • Fisette, T., P. Rollin, Z. Aly, L. Campbell, B. Daneshfar, P. Filyer, and I. Jarvis. 2013. “AAFC Annual Crop Inventory: Status and Challenges.” The Second International Conference on Agro-Geoinformatics, Fairfax, VA, USA.
  • Friedl, M. A., and C. E. Brodley. 1997. “Decision Tree Classification of Land Cover from Remotely Sensed Data.” Remote Sensing of Environment 61: 399–409. doi:10.1016/S0034-4257(97)00049-7.
  • Government of Canada. 2017. Accessed March 25 2017. http://open.canada.ca/en/open-data
  • Jiao, X., J. M. Kovacs, J. Shang, H. McNairn, D. Walters, B. Ma, and X. Geng. 2014. “Object-Oriented Crop Mapping and Monitoring Using Multi-Temporal Polarimetric RADARSAT-2 Data.” ISPRS Journal of Photogrammetry and Remote Sensing 96: 38–46. doi:10.1016/j.isprsjprs.2014.06.014.
  • Jolliffe, I. 2005. Principal Component Analysis. Hoboken, NJ: John Wiley & Sons, Ltd.
  • Ju, J., E. D. Kolaczyk, and S. Gopal. 2003. “Gaussian Mixture Discriminant Analysis and Sub-Pixel Land Cover Characterization in Remote Sensing.” Remote Sensing of Environment 84 (4): 550–560. doi:10.1016/S0034-4257(02)00172-4.
  • Lobo, A. 1997. “Image Segmentation and Discriminant Analysis for the Identification of Land Cover Units in Ecology.” IEEE Transactions on Geoscience and Remote Sensing 35 (5): 1136–1145. doi:10.1109/36.628781.
  • Mariotto, I., P. S. Thenkabail, A. Huete, E. T. Slonecker, and A. Platonov. 2013. “Hyperspectral versus Multispectral Crop-Productivity Modeling and Type Discrimination for the Hyspiri Mission.” Remote Sensing of Environment 139: 291–305. doi:10.1016/j.rse.2013.08.002.
  • McNairn, H., C. Champagne, J. Shang, D. Holmstrom, and G. Reichert. 2009. “Integration of Optical and Synthetic Aperture Radar (SAR) Imagery for Delivering Operational Annual Crop Inventories.” ISPRS Journal of Photogrammetry and Remote Sensing 64 (5): 434–449. doi:10.1016/j.isprsjprs.2008.07.006.
  • Nitze, I., B. Barrett, and F. Cawkwell. 2015. “Temporal Optimisation of Image Acquisition for Land Cover Classification with Random Forest and MODIS Time-Series.” International Journal of Applied Earth Observation and Geoinformation 34: 136–146. doi:10.1016/j.jag.2014.08.001.
  • Pal, M., and P. M. Mather. 2003. “An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification.” Remote Sensing of Environment 86 (4): 554–565. doi:10.1016/S0034-4257(03)00132-9.
  • Pal, N. R., and S. K. Pal. 1993. “A Review on Image Segmentation Techniques.” Pattern Recognition 26 (9): 1277–1294. doi:10.1016/0031-3203(93)90135-J.
  • Palacios-Orueta, A., and S. L. Ustin. 1998. “Remote Sensing of Soil Properties in the Santa Monica Mountains I. Spectral Analysis.” Remote Sensing of Environment 65 (2): 170–183. doi:10.1016/S0034-4257(98)00024-8.
  • Peerbhay, K. Y., O. Mutanga, and R. Ismail. 2013. “Commercial Tree Species Discrimination Using Airborne AISA Eagle Hyperspectral Imagery and Partial Least Squares Discriminant Analysis (PLS-DA) in Kwazulu–Natal, South Africa.” ISPRS Journal of Photogrammetry and Remote Sensing 79: 19–28. doi:10.1016/j.isprsjprs.2013.01.013.
  • Peña-Barragán, J. M., M. K. Ngugi, R. E. Plant, and J. Six. 2011. “Object-Based Crop Identification Using Multiple Vegetation Indices, Textural Features and Crop Phenology.” Remote Sensing of Environment 115 (6): 1301–1316. doi:10.1016/j.rse.2011.01.009.
  • Rezzi, S., D. E. Axelson, K. Héberger, F. Reniero, C. Mariani, and C. Guillou. 2005. “Classification of Olive Oils Using High Throughput Flow 1H NMR Fingerprinting with Principal Component Analysis, Linear Discriminant Analysis and Probabilistic Neural Networks.” Analytica Chimica Acta 552 (1): 13–24. doi:10.1016/j.aca.2005.07.057.
  • Richards, J. A., and X. Jia. 2006. Remote Sensing Digital Image Analysis, An Introduction. 4th ed. ed. Berlin: Springer.
  • Salehi, B., B. Daneshfar, and A. Davidson. 2017. “Accurate Crop - Type Classification Using Multi-Temporal Optical and Polarimetric SAR Data in an object-based image analysis framework.” International Journal of Remote Sensing 38(14): 4130–4155. doi:10.1080/01431161.2017.1317933.
  • Tatsumi, K., Y. Yamashiki, M. A. C. Torres, and C. L. R. Taipe. 2015. “Crop Classification of Upland Fields Using Random Forest of Time-Series Landsat 7 ETM+ Data.” Computers and Electronics in Agriculture 115: 171–179. doi:10.1016/j.compag.2015.05.001.
  • van der Linden, S., A. Rabe, M. Held, B. Jakimow, P. J. Leitão, A. Okujeni, M. Schwieder, S. Suess, and P. Hostert. 2015. “The Enmap-Box—A Toolbox and Application Programming Interface for Enmap Data Processing.” Remote Sensing 7 (9): 11249–11266. doi:10.3390/rs70911249.
  • van Zyl, J. J., and C. F. Burnette. 1992. “Bayesian Classification of Polarimetric SAR Images Using Adaptive a Priori Probabilities.” International Journal of Remote Sensing 13 (5): 835–840. doi:10.1080/01431169208904157.

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