1,241
Views
165
CrossRef citations to date
0
Altmetric
Technical Note

Crop classification by support vector machine with intelligently selected training data for an operational application

&
Pages 2227-2240 | Received 06 Dec 2005, Accepted 05 Apr 2007, Published online: 25 Mar 2008

References

  • Arora , M. K. and Foody , G. M. 1997 . Log‐linear modelling for the evaluation of the variables affecting the accuracy of probabilistic, fuzzy and neural network classifications. . International Journal of Remote Sensing , 18 : 785 – 798 .
  • Belousov , A. I. , Verzakov , S. A. and von Frese , J. 2002 . A flexible classification approach with optimal generalisation performance: support vector machines. . Chermometrics and Intelligent Laboratory Systems , 64 : 15 – 25 .
  • Buchheim , M. P. and Lillesand , T. M. 1989 . Semi‐automated training field extraction and analysis for efficient digital image classification. . Photogrammetric Engineering and Remote Sensing , 55 : 1347 – 1355 .
  • Campbell , J. B. 2002 . Introduction to Remote Sensing , London : Taylor and Francis . 3rd edn
  • Chen , D. M. and Stow , D. 2002 . The effect of training strategies on supervised classification at different spatial resolutions. . Photogrammetric Engineering and Remote Sensing , 68 : 1155 – 1161 .
  • Congalton , R. G. 1991 . A review of assessing the accuracy of classifications of remotely sensed data. . Remote Sensing of Environment , 37 : 35 – 46 .
  • Congalton , R. G. and Mead , R. A. 1983 . A quantitative method to test for consistency and correctness in photointerpretation. . Photogrammetric Engineering and Remote Sensing , 49 : 69 – 74 .
  • Curran , P. 1980 . Multispectral remote sensing of vegetation amount. . Progress in Physical Geography , 4 : 315 – 341 .
  • Foody , G. M. 1999 . The significance of border training patterns in classification by a feedforward neural network using back propagation learning. . International Journal of Remote Sensing , 20 : 3549 – 3562 .
  • Foody , G. M. 2002 . Status of land cover classification accuracy assessment. . Remote Sensing of Environment , 80 : 185 – 201 .
  • Foody , G. M. 2004 . Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. . Photogrammetric Engineering and Remote Sensing , 70 : 627 – 633 .
  • Foody , G. M. 2007 . “ Harshness in image classification accuracy assessment. ” . In International Journal of Remote Sensing Vol. 29 , (in press)
  • Foody , G. M. and Arora , M. K. 1997 . An evaluation of some factors affecting the accuracy of classification by an artificial neural network. . International Journal of Remote Sensing , 18 : 799 – 810 .
  • Foody , G. M. and Mathur , A. 2004a . A relative evaluation of multiclass image classification by support vector machine. . IEEE Transactions Geoscience Remote Sensing , 42 : 1335 – 1343 .
  • Foody , G. M. and Mathur , A. 2004b . Toward intelligent training of supervised image classifiactions: directing training data acquisition for SVM classification. . Remote Sensing of Environment , 93 : 107 – 117 .
  • Foody , G. M. , Mathur , A. , Sanchez‐Hernandez , C. and Boyd , D. S. 2006 . Training set size requirements for the classification of a specific class. . Remote Sensing of Environment , 104 : 1 – 14 .
  • Foody , G. M. , McCulloch , M. B. and Yates , W. B. 1995 . The effect of training set size and composition on artificial neural network classification. . International Journal of Remote Sensing , 16 : 1707 – 1723 .
  • Gualtieri , J. A. and Cromp , R. F. 1998 . Support vector machines for hyperspectral remote sensing classification. . Proceedings SPIE , 3584 : 221 – 232 .
  • Hixson , M. , Scholz , D. and Fuhs , N. 1980 . Evaluation of several schemes for classification of remotely sensed data. . Photogrammetric Engineering and Remote Sensing , 46 : 1547 – 1553 .
  • Ho , K. T. , Hull , J. J. and Srihari , S. N. 1994 . Decision combination in multiple classification systems. . IEEE Transactions on Pattern Analysis and Machine Analysis , 16 : 66 – 75 .
  • Hsu , C. W. and Lin , C. J. 2002 . A comparison of methods for multiclass support vector machines. . IEEE Transactions on Neural Networks , 13 : 415 – 425 .
  • Huang , C. , Davis , L. S. and Townshend , J. R. G. 2002 . An assessment of support vector machines for land cover classification. . International Journal of Remote Sensing , 23 : 725 – 749 .
  • Jackson , Q. and Landgrebe , D. A. 2001 . An adaptive classifier design for high‐dimensional data analysis with a limited training data set. . IEEE Transactions on Geoscience and Remote Sensing , 39 : 2664 – 2679 .
  • Mather , P. M. 2004 . Computer Processing of Remotely‐Sensed Images: An Introduction , Chichester, , UK : Wiley . 4th edn
  • Mathur , A. 2005 . “ Land cover classification: refining training requirements for support vector machine classification using remotely sensed data, ” . unpublished PhD thesis, School of Geography, University of Southampton, Southampton
  • Melgani , F. and Bruzzone , L. 2004 . Classification of hyperspectral remote sensing images with support vector machines. . IEEE Transactions Geoscience Remote Sensing , 42 : 1778 – 1790 .
  • Neumann , J. , Schnorr , C. and Steidl , G. 2005 . Combined SVM‐based feature selection and classification. . Machine Learning , 61 : 129 – 150 .
  • Pal , M. and Mather , P. M. 2003 . An assessment of the effectiveness of decision tree methods for land cover classification. . Remote Sensing of Environment , 86 : 554 – 565 .
  • Pal , M. and Mather , P. M. 2004 . Assessment of the effectiveness of support vector machines for hyperspectral data. . Future Generation Computer Systems , 20 : 1215 – 1225 .
  • Vapnik , V. N. 1995 . The Nature of Statistical Learning Theory , New York : Springer‐Verlag .
  • Wilkinson , G. G. 1996 . “ Classification algorithms – where next? ” . In Soft Computing in Remote Sensing Data Analysis , Edited by: Binaghi , E , Brivio , P. A and Rampini , A . 93 – 99 . Singapore : World Scientific .
  • Yadav , M. , Hooda , R. S. , Mothi Kumar , K. E. , Ruhal , D. S. , Khera , A. P. , Sigh , C. P. , Hooda , I. S. , Verma , U. , Dutta , S. and Kalubarme , M. H. 1995 . “ Cotton acreage estimation in Hissar and Sirsa districts of Haryana using IRS LISS‐I digital data. ” . In Proceedings of the National Symposium on Remote Sensing of Environment with Special Emphasis on Green Revolution , Edited by: Sahai , B , Sharma , P. K , Bhan , S. K , Parihar , J. S , Ravindran , K. V and Jayaraman , V . 30 – 34 . Ludhiana, , India : P.L. Printers . 22–24 November, Ludhiana (Punjab), India,
  • Zhuang , X. , Engel , B. A. , Lonzanogarcia , D. F. , Fernandez , R. N. and Johannsen , C. J. 1994 . Optimization of training data required for neuro‐classification. . International Journal of Remote Sensing , 15 : 3271 – 3277 .

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.