566
Views
29
CrossRef citations to date
0
Altmetric
Articles

Accurate crop-type classification using multi-temporal optical and multi-polarization SAR data in an object-based image analysis framework

, & ORCID Icon
Pages 4130-4155 | Received 15 Jun 2015, Accepted 30 Mar 2017, Published online: 10 May 2017

References

  • Baatz, M., and A. Schäpe. 2000. Multi-Resolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation. In Angewandte Geographische Informationsverarbeitung XII, Beiträge Zum AGIT-Symposium Salzburg. J. Strobl, T. Blaschke, and G. G. Ebner Eds. Hiedelberg:Wichman Verlag. 12–23. Salzburg, Austria
  • Ban, Y. 2003. “Synergy of Multitemporal ERS-1 SAR and Landsat TM Data for Classification of Agricultural Crops.” Canadian Journal of Remote Sensing 29 (4): 518–526. doi:10.5589/m03-014.
  • 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.
  • Blaes, X., L. Vanhalle, and P. Defourny. 2005. “Efficiency of Crop Identification Based on Optical and SAR Image Time Series.” Remote Sensing of Environment 96 (3): 352–365. doi:10.1016/j.rse.2005.03.010.
  • Breiman, L. 1996. “Bagging Predictors.” Machine Learning 24 (2): 123–140. doi:10.1007/BF00058655.
  • Brisco, B., and R. J. Brown. 1995. “Multidate SAR/TM Synergism for Crop Classification in Western Canada.” Photogrammetric Engineering and Remote Sensing 61 (8): 1009–1014.
  • Chen, E., Z. Li, Y. Pang, and X. Tian. 2007. “Quantitative Evaluation of Polarimetric Classification for Agricultural Crop Mapping.” Photogrammetric Engineering & Remote Sensing 73 (3): 279–284. doi:10.14358/PERS.73.3.279.
  • 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., and K. Green. 2009. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd ed. Boca Raton, FL, USA: CRC Press, Taylor & Francis Group.
  • Corcoran, P., A. Winstanley, and P. Mooney. 2010. “Segmentation Performance Evaluation for Object-Based Remotely Sensed Image Analysis.” International Journal of Remote Sensing 31 (3): 617–645. doi:10.1080/01431160902894475.
  • Del Frate, F., G. Schiavon, D. Solimini, M. Borgeaud, and M. A. M. Hoekman Vissers. 2003. “Crop Classification Using Multiconfiguration C-Band SAR Data.” IEEE Transactions Geosci Remote Sensing 41 (7): 1611–1619. doi:10.1109/TGRS.2003.813530.
  • 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 Remote Sensing 38 (1): 60–68. doi:10.5589/m12-012.
  • Devadas, R., R. J. Denham, and M. Pringle. 2012. “Support Vector Machine Classification of Object-Based Data for Crop Mapping Using Multi-Temporal Landsat Imagery.” In International Archive of the Photogrammetry, Remote Sensing and Spatial Information Science, XXXIX-B7, 185–190. Melbourne, Victoria: Copernicus.
  • Dey, V., Y. Zhang, M. Zhong, and B. Salehi. 2013. “Image Segmentation Techniques for Urban Land Cover Segmentation of VHR Imagery: Recent Developments and Future Prospects.” International Journal of Geoinformatics 9 (4): 15–35.
  • eCognition. 2014. Ecognition Developer Reference Book (Ver. 9.0.1), Trimble Germany Gmbh, Arnulfstrasse 126, D-80636. Munich: Germany.
  • Feller, W. 1968. An Introduction to Probability Theory and Its Application. third ed. New York, USA: Willey.
  • Fisette, T., R. Chenier, M. Maloley, P. Y. Gasser, T. Huffman, L. White, R. Ogston, and A. Elgarawany, 2006. Methodology for a Canadian Agricultural Land Cover Classification. In Proceedings of 1st International Conference on Object-based Image Analysis, Salzburg, Austria, July 2006; In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2006; Volume XXXVI 4/C42.
  • Fisette, T., A. Davidson, B. Daneshfar, P. Rollin, and L. Campbell, 2014. Annual Space-Based Crop Inventory for Canada: 2009-2014. Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, Quebec City, QC, pp. 5095–5098.
  • Fisette, T., P. Rollin, Z. Aly, L. Campbell, B. Daneshfar, P. Filyer, A. Smith, A. Davidson, J. Shang, and I. Jarvis, 2013. AAFC Annual Crop Inventory: Status and Challenges. The 2nd International Conference on Agro-Geoinformatics, Fairfax, VA, pp. 270–274.
  • Foody, G. M., M. B. McCulloch, and W. B. Yates. 1994. “Crop Classification from C-Band Polarimetric Radar Data.” International Journal of Remote Sensing 15 (14): 2871–2885. doi:10.1080/01431169408954289.
  • Forster, D., T. W. Kellenberger, Y. Buehler, and B. Lennartz. 2010. “Mapping Diversified Peri-Urban Agriculture – Potential of Object-Based versus Per-Field Land Cover/Land Useclassification.” Geocarto International 25 (3): 171–186. doi:10.1080/10106040903243416.
  • 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.
  • Gallego, F. J. 2004. “Remote Sensing and Land Cover Area Estimation.” International Journal Remote Sens 25: 3019–3047. doi:10.1080/01431160310001619607.
  • Gallego, F. J. 2006. Review of the Main Remote Sensing Methods for Crop Area Estimates. ISPRS Archives XXXVI-8/W48 Workshop proceedings: Remote sensing support to crop yield forecast and area estimates, Stresa, Italy, pp. 65–70.
  • Haralick, R. M., K. Shanmugan, and I. Dinstein. 1973. “Textural Features for Image Classi-Fication.” IEEE Transactions on Systems, Manual and Cybernetics SMC-3 (6): 610–621. doi:10.1109/TSMC.1973.4309314.
  • Hoberg, T., F. Rottensteiner, R. Q. Feitosa, and C. Heipke. 2015. “Conditional Random Fields for Multitemporal and Multiscale Classification of Optical Satellite Imagery.” IEEE Transactions on Geoscience and Remote Sensing 33 (2): 659–673. doi:10.1109/TGRS.2014.2326886.
  • Hoekman, D. H., and M. A. M. Vissere. 2003. “A New Polarimetric Classification Approach Evaluated for Agricultural Crops.” IEEE Transactions Geosci Remote Sens 41 (12): 2881–2889. doi:10.1109/TGRS.2003.817795.
  • Hong, G., S. Wang, J. Li, and J. Huang. 2015. “Fully Polarimetric Synthetic Aperture Radar (SAR) Processing for Crop Type Classification.” Photogrammetric Engineering & Remote Sensing 81 (2): 109–117. doi:10.14358/PERS.81.2.109.
  • Hughes, G. F. 1968. “On the Mean Accuracy of Statistical Pattern Recognizers.” IEEE Transactions on Information Theory 14: 55–63. doi:10.1109/TIT.1968.1054102.
  • Jiao, X., J. M. Kovacs, J. Shang, H. McNairn, D. Walters, B. Ma, and G. Geng. 2014. “Object-Orinted 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.
  • Joelsson, S. R., J. A. Benediktsson, and J. R. Sveninsson. 2006. “Random Forest Classification of Remote Sensing Data.” In Image Processing for Remote Sensing, 61–78. Boca Racon, FL, USA: CRC Press.
  • Kim, H.-O., and J.-M. Yeom. 2014. “Effect of Red-Edge and Texture Features for Object-Based Paddy Rice Crop Classification Using Rapideye Multi-Spectral Satellite Image Data.” International Journal of Remote Sensing 35 (19): 7046–7068.
  • Lee, J. S., M. R. Grunes, and G. De Grandi. 1999. “Polarimetric SAR Speckle Filtering and Its Implication for Classification.” IEEE Transactions on Geoscience and Remote Sensing 37 (5): 2363–2373. doi:10.1109/36.789635.
  • Lee, J.-S., and E. Pottier. 2009. Polarimetric Radar Imaging: From Basics to Applications. Boca Racon, FL, USA: CRC press.
  • Li, K., B. Brisco, Y. Shao, and R. Touzi. 2012. “Polarimetric Decomposition with Radarsat-2 for Rice Mapping and Monitoring.” Canadian Journal of Remote Sensing 38 (2): 169–179. doi:10.5589/m12-024.
  • Long, J. A., R. L. Lawarence, M. C. Greenwood, L. Marshall, and P. P. Miller. 2013. “Object-Oriented Crop Classification Using Multitemporal ETM + Slc-Off Imagery and Random Forest.” Gisceince & Remote Sensing 50 (4): 418–436.
  • Mather, P. M., and M. Koch. 2011. Computer Processing of Remotely-Sensed Images: An Introduction, 460. 4th ed. Chichester: Wiley.
  • 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: 434–449. doi:10.1016/j.isprsjprs.2008.07.006.
  • McNairn, H., J. Ellis, J. J. Van Der Sanden, T. Hirose, and R. J. Brown. 2002. “Providing Crop Information Using Radarsat-1 and Satellite Optical Imagery.” International Journal of Remote Sensing 23 (5): 851–870. doi:10.1080/01431160110070753.
  • Oetter, D. R., W. B. Cohen, M. Berterretche, T. K. Maiersperger, and R. E. Kennedy. 2000. “Land Cover Mapping in an Agricultural Setting Using Multiseasonal Thematic Mapper Data.” Remote Sensing of Environment 76: 139–155. doi:10.1016/S0034-4257(00)00202-9.
  • 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 (3): 1277–1294. doi:10.1016/0031-3203(93)90135-J.
  • Peters, J., F. V. Coillie, T. Westra, and R. D. Wulf. 2013. “Synergy of Very High Resolution Optical and Radar Data for Object-Based Olive Grove Mapping.” International Journal of Geographical Information Science 25 (6): 971–989. doi:10.1080/13658816.2010.515946.
  • RapidEye, 2013. Satellite Imagery Product Specification_V.6.0. Available online: http://blackbridge.com/rapideye/upload/RE_Product_Specifications_ENG.pdf (last access: March 2015).
  • Richards, J. A., and X. Jia. 2006. Remote Sensing Digital Image Analysis, An Introduction, 464 p. Fourth ed. Berlin: Springer.
  • Rodriguez-Galiano, V. F., B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez. 2012. “An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 67: 93–104. doi:10.1016/j.isprsjprs.2011.11.002.
  • RuleQuest Research. 2008. See5. In Rulequest Research Pty. Sydney: Ltd.
  • Salehi, B., M. ValadanZoej, and M. Varshosaz. 2008. “Projection Pursuit and Lowpass Filtering for Pre-Processing of Hyperspectral Images.” World Applied Sciences Journal 3 (5): 785–796.
  • Salehi, B., Y. Zhang, and M. Zhong. 2013. “A Combined Pixel-And Object-Based Image Analysis Framework for Urban Land Cover Classification of VHR Imagery.” Photogrammetric Engineering & Remote Sensing 79 (11): 999–1014. doi:10.14358/PERS.79.11.999.
  • Salehi, B., Y. Zhang, M. Zhong, and V. Dey. 2012a. “A Review of the Effectiveness of Spatial Information Used in Urban Land Cover Classification of VHR Imagery.” International Journal of Geoinformatics 8 (2): 35–51.
  • Salehi, B., Y. Zhang, M. Zhong, and V. Dey. 2012b. “Object-Based Classification of Urban Areas Using VHR Imagery and Height Points Ancillary Data.” Remote Sensing 4: 2256–2276. doi:10.3390/rs4082256.
  • Senay, G. B., J. G. Lyon, A. D. Ward, and S. E. Nokes. 2000. “Using High Spatial Resolution Multispectral Data to Classify Corn and Soybean Crops.” Photogrammetric Engineering & Remote Sensing 66 (3): 319–327.
  • Shang, J., H. McNairn, C. Champagne, and X. Jiao, 2008. Contribution of Multifrequency, Multi-Sensor, and Multi-Temporal Radar Data to Operational Annual Crop Mapping. In: Proc. IGARSS Annual Conference, Boston, MA, July 8–11, 378–381.
  • Simonneaux, V., B. Duchemin, D. Helson, S. Er-Raki, A. Olioso, and A. G. Chehbouni. 2008. “The Use of High-Resolution Image Time Series for Crop Classification and Evapotranspiration Estimate over an Irrigated Area in Central Morocco.” International Journal of Remote Sensing 29 (1): 95–116. doi:10.1080/01431160701250390.
  • Skriver, H. 2012. “Crop Classification by Multitemporal C-And L-Band Single-And Dual-Polarization and Fully Polarimetric SAR.” IEEE Transactions on Geoscience and Remote Sensing 50 (6): 2138–2149. doi:10.1109/TGRS.2011.2172994.
  • Tan, C. P., H. T. Ewe, and H. T. Chuah. 2011. “Agricultural Crop-Type Classification of Multi- Polarization SAR Images Using a Hybrid Entropy Decomposition and Support Vector Machine Technique.” International Journal of Remote Sensing 32 (22): 7057–7071. doi:10.1080/01431161.2011.613414.
  • Tso, B., and P. M. Mather. 1999. “Crop Discrimination Using Multi-Temporal SAR Imagery.” International Journal of Remote Sensing 20 (12): 2443–2460. doi:10.1080/014311699212119.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.