Research Article
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Comparison of Object and Pixel-Based Classifications For Mapping Crops Using Rapideye Imagery: A Case Study Of Menemen Plain, Turkey

Year 2018, Volume: 5 Issue: 2, 231 - 243, 01.08.2018
https://doi.org/10.30897/ijegeo.442002

Abstract

With the latest
development and increasing availability of high spatial resolution sensors,
earth observation technology offers a viable solution for crop identification
and management. There is a strong need to produce accurate, reliable and up to
date crop type maps for sustainable agriculture monitoring and management. In
this study, RapidEye, the first high-resolution multi-spectral satellite system
that operationally provides a Red-edge channel, was used to test the potential
of the data for crop type mapping. This study was investigated at a selected
region mostly covered with agricultural fields locates in the low lands of
Menemen (İzmir) Plain, TURKEY. The potential of the three classification
algorithms such as Maximum Likelihood Classification, Support Vector Machine
and Object Based Image Analysis is
tested. Accuracy assessment of land cover maps has been performed
through error matrix and kappa indexes. The results highlighted that all
selected classifiers as highly useful (over 90%) in mapping of crop types in
the study region however the object-based approach slightly outperforming the
Support Vector Machine classification by both overall accuracy and Kappa
statistics. The success of selected methods also underlines the potential of
RapidEye data for mapping crop types in Aegean region.

References

  • Townshend, J.R.G. “Land cover,” International Journal of Remote Sensing, 13, 1319-1328, (1992).
  • Pal, M. and Mather, P.M. “Assessment of the Effectiveness of Support Vector Machines For Hyperspectral Data” Future Generation Computer Systems, 20, 1215–1225, (2004).
  • Lu, D. and Weng, Q., “A Survey of Image Classification Methods and Techniques for Improving Classification Performance” International Journal of Remote Sensing 28 (5), 823–870, (2007).
  • Kaya, S., Pekin, F., Seker, D. Z., Tanik, A. “An Algorithm Approach for the Analysis of Urban Land-Use/Cover: Logic Filters” International Journal of Environment and Geoinformatics 1(1-3), pp: 12-20, (2014).
  • Islam, K., Jasimuddin, M., Nath, B., Nath, K. T.”Quantitative Assessment of Land Cover Change Using Landsat Time Series Data: Case of Chunati Wildlife Sanctuary (CWS), Bangladesh” International Journal of Environment and Geoinformatics. 3 (2), pp: 45-55 (2016)
  • Goksel, C., David, R. M. and Dogru, A.O. “Environmental Monitoring of Spatio-Temporal Changes in Northern Istanbul using Remote Sensing and GIS” International Journal of Environment and Geoinformatics 5(1), pp:94-103, (2018).
  • Pao, Y. H. “Adaptive Pattern Recognition and Neural Networks” Reading, MA: Addison-Wesley Publishing Company, ISBN 0-201-12584-6, (1989).
  • Quinlan, J. R. “C4.5 Programs for Machine Learning,” San Mateo, CA: Morgan Kaufmann Publishers, (1993)
  • Pal, M. “Advanced algorithms for land use and cover Classification” Advances in Mapping from Remote Sensor Imagery,” CRC Press: 69-90. (2012)
  • Hansen, M., Dubayah, R. and R. DeFries. “ClassificationTrees: An Alternative to Traditional land cover Classifiers” International Journal of Remote Sensing, 17, 1075–1081, (1996).
  • Huang, C., Davis, L.S. and Townshend, J.R.G. “An Assessment of Support Vector Machines For Land Cover Classification,” International Journal of Remote Sensing, 23, 725–749, (2002).
  • Zhang, Y., Gao, J. and Wang, J. “Detailed Mapping Of A Salt Farm From Landsat TM Imagery Using Neural Network And Maximum Likelihood Classifiers: A Comparison” International Journal of Remote Sensing, 28, 2077–2089, (2007).
  • Benediktsson, J.A., Swain, P.H. and Erase, O.K. “Neural Network Approaches Versus Statistical Methods In Classification of Multisource Remote Sensing Data” IEEE Transactions on Geoscience and Remote Sensing 28, 540–551, (1990).
  • Tso, B.K.C. and Mather, P.M. “Classification Methods For Remotely Sensed Data,” London: Taylor and Francis, (2001).
  • Safavian, S. R. and Landgrebe, D. “A Survey Of Decision Tree Classifier Methodology,” IEEE T ransactions on Systems, Man, and Cybernetics, 21, 660–674, (1991).
  • Friedl, M. A. and Brodley, C. E. “Decision Tree Classification of Land Cover From Remotely Sensed Data,” Remote Sensing of Environment, 61, 399–409, (1997).
  • Brodley, C. E. and Utgoff, P. E. “Multivariate Decision Tree,” Machine Learning, 19, 45–77, (1995).
  • Tao, H., et al., “Enhanced Land Use/Cover Classification Using Support Vector Machines And Fuzzy K-Means Clustering Algorithms,” Journal of Applied Remote Sensing 8(01), 1-14, (2014).
  • Hosseini, R. S., Homayouni, S., Safari, R. “Modified Algorithm Based On Support Vector Machines For Classification Of Hyperspectral İmages İn A Similarity Space,” Journal of Applied. Remote Sensing, 6(1), 063550, (2012).
  • Yang, C., Everitt, J.H., and Murden, D. "Evaluating high resolution SPOT 5 satellite imagery for crop identification." Computers and Electronics in Agriculture 75(2), 347-354, (2011).
  • Barragan P. et al. “Object-Based Crop Identification Using Multiple Vegetation İndices, Textural Features And Crop Phenology,” Remote Sensing of Environment, 115(6), 1301-1316, (2011).
  • Wuest, B. and Zhang, Y. “Region Based Segmentation Of Quickbird Multispectral Imagery Through Band Ratios And Fuzzy Comparison,” ISPRS J.l of Photo. and Remote Sensing, 64, 55-64, (2009).
  • Blaschke, T. “Object Based Image Analysis For Remote Sensing,” ISPRS J.l of Photo. and Remote Sensing 65, 2-16, (2010).
  • Whiteside, T. G. et al. "Comparing Object-Based And Pixel-Based Classifications For Mapping Savannas," Int. Journal of Applied Earth Observation and Geoinformation 13(6), 884-893, (2011).
  • Gutiérrez, A. J. et al., "Optimizing Land Cover Classification Accuracy For Change Detection, A Combined Pixel-Based And Object-Based Approach In A Mountainous Area In Mexico," Applied Geography 34(0), 29-37, (2012).
  • Jebur, M. N. et al., "Per-Pixel And Object-Oriented Classification Methods For Mapping Urban Land Cover Extraction Using SPOT 5 Imagery," Geocarto Internationa, 1-15-27, (2013).
  • Sandau, R., Brieß, K., D’Errico, M. “Small Satellites For Global Coverage: Potential Limits,” ISPRS Journal of Photogrammetry and Remote Sensing, 65, 92–504, (2010).
  • Liu, J. G. and Mason, P. J. “Essential Image Processing and GIS for Remote Sensing,” John Wiley & Sons, Inc., (2009).
  • Gong, P. “Remote Sensing and Image Analysis Textbook”, http://nature.berkeley.edu/~penggong/textbook/chapter7/html/sect73.htm, (2002), (accessed:June, 2014)
  • Vapnik, V.N. “The Nature of Statistical Learning Theory,” New York: Springer-Verlag, (1995).
  • Mather, P. M. and M. Koch, “Classification,” Computer Processing of Remotely-Sensed Images, John Wiley & Sons, Ltd: 229-284, (2011).
  • Burges, C.J.C. “A Tutorial On Support Vector Machines For Pattern Recognition,” Data Mining and Knowledge Discovery 2 (2), 121–167, (1998).
  • Shao, Y. and Lunetta, R. S. "Comparison Of Support Vector Machine, Neural Network, And CART Algorithms For The Land-Cover Classification Using Limited Training Data Points." ISPRS Journal of Photogrammetry and Remote Sensing 70(0), 78-87, (2012).
  • Foody, G.M. and Mathur, A. “Toward Intelligent Training Of Supervised Image Classifications: Directing Training Data Acquisition For SVM Classification,” Remote Sensing of Environment, 93, 107-117, (2004).
  • Kavzoglu, T. and Colkesen, I. “A Kernel Functions Analysis For Support Vector Machines For Land Cover Classification,” Int. Journal of Application Earth Observation and Geoinformation. 11(5), 352–359, (2009).
  • Chang, C., C. and Lin J. C. LIBSVM library adapted by ITT Visual Information Solutions (www.csie.ntu.edu.tw/~cjlin/libsvm) (2014), (accessed: April 2014).
  • Walsh, S. J. et al., “Quickbird And Hyperion Data Analysis of An Invasive Plant Species In The Galapagos Islands of Ecuador: Implications For Control And Land Use Management,” Remote Sensing of Environment, 112(5), 1927–1941, (2008).
  • Kok, R., Schneider, T. & Ammer, U. “Object-Based Classification And Applications In The Alpine Forest Environment,” International Archives of Photogrammetry and Remote Sensing, (1999).
  • Bock, M. et al., “Object-Oriented Methods For Habitat Mapping At Multiple Scales-Case Studies From Northern Germany And Wye Downs,” UK. J. for Nature Conservation, 13(2-3), 75–89, (2005).
  • Tarabalka, Y., Chanussot, J., Benediktsson, J. A. “Segmentation And Classification Of Hyperspectral Images Using Watershed Transformation,” Pattern Recognition, 43(7), 2367–2379, (2010).
  • Jr. Rouse J.W. et. al., “Monitoring The Vernal Advancement And Retrogradation (Green Wave Effect) of Natural Vegetation,” NASA/GSFC Type III Final Report, Greenbelt, MD., 371p, (1974).
  • Barnes, E.M., Chang, J., Clay, S.A., Clay David E. C., and Dalsted, K. “Coincident Detection Of Crop Water Stress, Nitrogen Status And Canopy Density Using Ground-Based Multispectral Data,” In P.C. Robert et al. (ed.) Proc. Int. Conf. Prec. Agric., 5th, Bloomington, MN, 16–19 July 2000, ASA, CSSA, and SSSA, Madison, WI, (2000).
  • Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., and Feng, J. “Remote Estimation Of Gross Primary Production In Wheat Using Chlorophyll-Related Vegetation Indices,” Agricultural and Forest Meteorology 149, 1015–1021, (2009).
  • Birth, G.S. and McVey, G. “Measuring The Color Of Growing Turf With A Reflectance Spectroradiometer,” Agronomy Journal. 60, 640-643, (1968).
  • Gao, B. “NDWI-A Normalized Difference Water Index For Remote Sensing Of Vegetation Liquid Water From Space,” Remote Sensing of Environment, 58(3), 257-266, (1996).
  • Foody,G.M. “Status Of Land Cover Classification Accuracy Assessment,” Remote Sensing of Environment, 80, 185–201, (2002).
  • Su, W. Zhang, C., Zhu, X. and Daoliang, L.“A Hierarchical Object Oriented Method For Land Cover Classification Of SPOT 5 Imagery,” WSEAS Trans Inform Sci Appl. 6(3):437–446, (2009).
  • Gao, Y. and Mas, J. F. “A Comparison of the Performance of Pixel Based and Object Based Classifications over Images with Various Spatial Resolutions,” Online J. of Earth Sci., 2: 27-35, (2008).
  • Gao,Y., Mas, J.F., Maathuis, B.H.P., Xiangmin, Z. and Van Dijk, P. M. “Comparison of Pixel-Based And Object-Oriented Image Classification Approaches-A Case Study In A Coal Fire Area, Mongolia, China,” Int. J. of Remote Sens, 27, 4039-4051, (2006).
Year 2018, Volume: 5 Issue: 2, 231 - 243, 01.08.2018
https://doi.org/10.30897/ijegeo.442002

Abstract

References

  • Townshend, J.R.G. “Land cover,” International Journal of Remote Sensing, 13, 1319-1328, (1992).
  • Pal, M. and Mather, P.M. “Assessment of the Effectiveness of Support Vector Machines For Hyperspectral Data” Future Generation Computer Systems, 20, 1215–1225, (2004).
  • Lu, D. and Weng, Q., “A Survey of Image Classification Methods and Techniques for Improving Classification Performance” International Journal of Remote Sensing 28 (5), 823–870, (2007).
  • Kaya, S., Pekin, F., Seker, D. Z., Tanik, A. “An Algorithm Approach for the Analysis of Urban Land-Use/Cover: Logic Filters” International Journal of Environment and Geoinformatics 1(1-3), pp: 12-20, (2014).
  • Islam, K., Jasimuddin, M., Nath, B., Nath, K. T.”Quantitative Assessment of Land Cover Change Using Landsat Time Series Data: Case of Chunati Wildlife Sanctuary (CWS), Bangladesh” International Journal of Environment and Geoinformatics. 3 (2), pp: 45-55 (2016)
  • Goksel, C., David, R. M. and Dogru, A.O. “Environmental Monitoring of Spatio-Temporal Changes in Northern Istanbul using Remote Sensing and GIS” International Journal of Environment and Geoinformatics 5(1), pp:94-103, (2018).
  • Pao, Y. H. “Adaptive Pattern Recognition and Neural Networks” Reading, MA: Addison-Wesley Publishing Company, ISBN 0-201-12584-6, (1989).
  • Quinlan, J. R. “C4.5 Programs for Machine Learning,” San Mateo, CA: Morgan Kaufmann Publishers, (1993)
  • Pal, M. “Advanced algorithms for land use and cover Classification” Advances in Mapping from Remote Sensor Imagery,” CRC Press: 69-90. (2012)
  • Hansen, M., Dubayah, R. and R. DeFries. “ClassificationTrees: An Alternative to Traditional land cover Classifiers” International Journal of Remote Sensing, 17, 1075–1081, (1996).
  • Huang, C., Davis, L.S. and Townshend, J.R.G. “An Assessment of Support Vector Machines For Land Cover Classification,” International Journal of Remote Sensing, 23, 725–749, (2002).
  • Zhang, Y., Gao, J. and Wang, J. “Detailed Mapping Of A Salt Farm From Landsat TM Imagery Using Neural Network And Maximum Likelihood Classifiers: A Comparison” International Journal of Remote Sensing, 28, 2077–2089, (2007).
  • Benediktsson, J.A., Swain, P.H. and Erase, O.K. “Neural Network Approaches Versus Statistical Methods In Classification of Multisource Remote Sensing Data” IEEE Transactions on Geoscience and Remote Sensing 28, 540–551, (1990).
  • Tso, B.K.C. and Mather, P.M. “Classification Methods For Remotely Sensed Data,” London: Taylor and Francis, (2001).
  • Safavian, S. R. and Landgrebe, D. “A Survey Of Decision Tree Classifier Methodology,” IEEE T ransactions on Systems, Man, and Cybernetics, 21, 660–674, (1991).
  • Friedl, M. A. and Brodley, C. E. “Decision Tree Classification of Land Cover From Remotely Sensed Data,” Remote Sensing of Environment, 61, 399–409, (1997).
  • Brodley, C. E. and Utgoff, P. E. “Multivariate Decision Tree,” Machine Learning, 19, 45–77, (1995).
  • Tao, H., et al., “Enhanced Land Use/Cover Classification Using Support Vector Machines And Fuzzy K-Means Clustering Algorithms,” Journal of Applied Remote Sensing 8(01), 1-14, (2014).
  • Hosseini, R. S., Homayouni, S., Safari, R. “Modified Algorithm Based On Support Vector Machines For Classification Of Hyperspectral İmages İn A Similarity Space,” Journal of Applied. Remote Sensing, 6(1), 063550, (2012).
  • Yang, C., Everitt, J.H., and Murden, D. "Evaluating high resolution SPOT 5 satellite imagery for crop identification." Computers and Electronics in Agriculture 75(2), 347-354, (2011).
  • Barragan P. et al. “Object-Based Crop Identification Using Multiple Vegetation İndices, Textural Features And Crop Phenology,” Remote Sensing of Environment, 115(6), 1301-1316, (2011).
  • Wuest, B. and Zhang, Y. “Region Based Segmentation Of Quickbird Multispectral Imagery Through Band Ratios And Fuzzy Comparison,” ISPRS J.l of Photo. and Remote Sensing, 64, 55-64, (2009).
  • Blaschke, T. “Object Based Image Analysis For Remote Sensing,” ISPRS J.l of Photo. and Remote Sensing 65, 2-16, (2010).
  • Whiteside, T. G. et al. "Comparing Object-Based And Pixel-Based Classifications For Mapping Savannas," Int. Journal of Applied Earth Observation and Geoinformation 13(6), 884-893, (2011).
  • Gutiérrez, A. J. et al., "Optimizing Land Cover Classification Accuracy For Change Detection, A Combined Pixel-Based And Object-Based Approach In A Mountainous Area In Mexico," Applied Geography 34(0), 29-37, (2012).
  • Jebur, M. N. et al., "Per-Pixel And Object-Oriented Classification Methods For Mapping Urban Land Cover Extraction Using SPOT 5 Imagery," Geocarto Internationa, 1-15-27, (2013).
  • Sandau, R., Brieß, K., D’Errico, M. “Small Satellites For Global Coverage: Potential Limits,” ISPRS Journal of Photogrammetry and Remote Sensing, 65, 92–504, (2010).
  • Liu, J. G. and Mason, P. J. “Essential Image Processing and GIS for Remote Sensing,” John Wiley & Sons, Inc., (2009).
  • Gong, P. “Remote Sensing and Image Analysis Textbook”, http://nature.berkeley.edu/~penggong/textbook/chapter7/html/sect73.htm, (2002), (accessed:June, 2014)
  • Vapnik, V.N. “The Nature of Statistical Learning Theory,” New York: Springer-Verlag, (1995).
  • Mather, P. M. and M. Koch, “Classification,” Computer Processing of Remotely-Sensed Images, John Wiley & Sons, Ltd: 229-284, (2011).
  • Burges, C.J.C. “A Tutorial On Support Vector Machines For Pattern Recognition,” Data Mining and Knowledge Discovery 2 (2), 121–167, (1998).
  • Shao, Y. and Lunetta, R. S. "Comparison Of Support Vector Machine, Neural Network, And CART Algorithms For The Land-Cover Classification Using Limited Training Data Points." ISPRS Journal of Photogrammetry and Remote Sensing 70(0), 78-87, (2012).
  • Foody, G.M. and Mathur, A. “Toward Intelligent Training Of Supervised Image Classifications: Directing Training Data Acquisition For SVM Classification,” Remote Sensing of Environment, 93, 107-117, (2004).
  • Kavzoglu, T. and Colkesen, I. “A Kernel Functions Analysis For Support Vector Machines For Land Cover Classification,” Int. Journal of Application Earth Observation and Geoinformation. 11(5), 352–359, (2009).
  • Chang, C., C. and Lin J. C. LIBSVM library adapted by ITT Visual Information Solutions (www.csie.ntu.edu.tw/~cjlin/libsvm) (2014), (accessed: April 2014).
  • Walsh, S. J. et al., “Quickbird And Hyperion Data Analysis of An Invasive Plant Species In The Galapagos Islands of Ecuador: Implications For Control And Land Use Management,” Remote Sensing of Environment, 112(5), 1927–1941, (2008).
  • Kok, R., Schneider, T. & Ammer, U. “Object-Based Classification And Applications In The Alpine Forest Environment,” International Archives of Photogrammetry and Remote Sensing, (1999).
  • Bock, M. et al., “Object-Oriented Methods For Habitat Mapping At Multiple Scales-Case Studies From Northern Germany And Wye Downs,” UK. J. for Nature Conservation, 13(2-3), 75–89, (2005).
  • Tarabalka, Y., Chanussot, J., Benediktsson, J. A. “Segmentation And Classification Of Hyperspectral Images Using Watershed Transformation,” Pattern Recognition, 43(7), 2367–2379, (2010).
  • Jr. Rouse J.W. et. al., “Monitoring The Vernal Advancement And Retrogradation (Green Wave Effect) of Natural Vegetation,” NASA/GSFC Type III Final Report, Greenbelt, MD., 371p, (1974).
  • Barnes, E.M., Chang, J., Clay, S.A., Clay David E. C., and Dalsted, K. “Coincident Detection Of Crop Water Stress, Nitrogen Status And Canopy Density Using Ground-Based Multispectral Data,” In P.C. Robert et al. (ed.) Proc. Int. Conf. Prec. Agric., 5th, Bloomington, MN, 16–19 July 2000, ASA, CSSA, and SSSA, Madison, WI, (2000).
  • Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., and Feng, J. “Remote Estimation Of Gross Primary Production In Wheat Using Chlorophyll-Related Vegetation Indices,” Agricultural and Forest Meteorology 149, 1015–1021, (2009).
  • Birth, G.S. and McVey, G. “Measuring The Color Of Growing Turf With A Reflectance Spectroradiometer,” Agronomy Journal. 60, 640-643, (1968).
  • Gao, B. “NDWI-A Normalized Difference Water Index For Remote Sensing Of Vegetation Liquid Water From Space,” Remote Sensing of Environment, 58(3), 257-266, (1996).
  • Foody,G.M. “Status Of Land Cover Classification Accuracy Assessment,” Remote Sensing of Environment, 80, 185–201, (2002).
  • Su, W. Zhang, C., Zhu, X. and Daoliang, L.“A Hierarchical Object Oriented Method For Land Cover Classification Of SPOT 5 Imagery,” WSEAS Trans Inform Sci Appl. 6(3):437–446, (2009).
  • Gao, Y. and Mas, J. F. “A Comparison of the Performance of Pixel Based and Object Based Classifications over Images with Various Spatial Resolutions,” Online J. of Earth Sci., 2: 27-35, (2008).
  • Gao,Y., Mas, J.F., Maathuis, B.H.P., Xiangmin, Z. and Van Dijk, P. M. “Comparison of Pixel-Based And Object-Oriented Image Classification Approaches-A Case Study In A Coal Fire Area, Mongolia, China,” Int. J. of Remote Sens, 27, 4039-4051, (2006).
There are 49 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

M. Tolga Esetlili

Filiz Bektas Balcik

Fusun Balik Sanli This is me

Kaan Kalkan This is me

Mustafa Ustuner

Cigdem Goksel

Cem Gazioğlu

Yusuf Kurucu

Publication Date August 1, 2018
Published in Issue Year 2018 Volume: 5 Issue: 2

Cite

APA Esetlili, M. T., Bektas Balcik, F., Balik Sanli, F., Kalkan, K., et al. (2018). Comparison of Object and Pixel-Based Classifications For Mapping Crops Using Rapideye Imagery: A Case Study Of Menemen Plain, Turkey. International Journal of Environment and Geoinformatics, 5(2), 231-243. https://doi.org/10.30897/ijegeo.442002

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