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Crop classification accuracy as influenced by training strategy, data transformation and spatial resolution of data

  • T T Medhavy
  • Tara Sharma
  • R P Dubey
  • R S Hooda
  • K E Mothikumar
  • M Yadav
  • M L Manchanda
  • D S Ruhal
  • A P Khera
  • S D Jarwal
Article

Abstract

The possibility of improving classification accuracies using different training strategies and data transformations within the framework of a supervised maximum likelihood classification scheme was explored in this study. The effect of spatial resolution of data on the accuracy of classification was also studied Single-pixel training strategy resulted in improved classification accuracy over the block-training method. Data transformations gave no significant improvements in accuracy over untransformed data. There was a reduction in classification accuracy as resolution of data improved from 72 m (LISS I) to 36 m (LISS II) while other sensor characteristics remained same.

Keywords

Remote Sensing Classification Accuracy Kappa Coefficient Data Transformation Training Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Campbell J B (1981). Spatial correlation effects upon accuracy of supervised classification of land cover, Photogramm. Eng. Remote Sensing, 47(3): 355–363.Google Scholar
  2. Chuveico E and Congalton R G (1988). Using clustering analysis to improve the selection of training statistics in classifying remotely sensed data, Photogramm. Eng. Remote Sensing, 54(9): 1275–1281.Google Scholar
  3. Cohen J (1960). A coefficient of agreement for nominal scales, Educational and psychological measurement, 20(1): 37–46CrossRefGoogle Scholar
  4. Craig R G (1979). Autocorrelation in Landsat data, In Proc. 13th Int. Symp. Remote Sensing Environ., ERIM (Ann Arbor. Michigan), pp. 1517–1524.Google Scholar
  5. Cushnie J L (1987). The interactive effect of spatial resolution and degree of internal variability within land-cover types on classification accuracies, Int. J. Remote Sensing, 8(1): 15–29.CrossRefGoogle Scholar
  6. Dadhwal V K and Parihar J S (1985). Estimation of 1983–84 wheat acreage of Karnal district (Haryana) using Landsat MSS digital data, Scientific Note, IRSUP/SAC/CPF/SN/09/85.Google Scholar
  7. Dadhwal V K, Parihar J S, Ruhal D S, Jarwal S D, Medhavy T T, Khera A P and Singh J (1989). Effect of acquisition date and TM spectral bands on wheat mustard and gram classification accuracies, J. Indian Soc. Remote Sensing, 17(4): 19–24.CrossRefGoogle Scholar
  8. Fleiss J L, Cohen J and Everitt B S (1969). Large sample standard errors of Kappa and weighted Kappa, Psychological bulletin, 72(5): 323–327.CrossRefGoogle Scholar
  9. Gong P and Howarth P J (1990). An assessment of some factors influencing multispectral land cover classification, Photogramm. Engg. Remote Sensing, 56(5): 597–603.Google Scholar
  10. Hixson M, Scholz D, Fuhs N and Akiyama T (1980). Evaluation of several schemes for classification of remotely sensed data, Photogramm. Eng. Remote Sensing, 46(12): 1547–1553.Google Scholar
  11. Labovitz M L and Masuoka E J (1984). The influence of autocorrelation in signature extraction—an example from a geobotanical investigation of Cotter basin, Montana, Int. J. Remote Sensing, 5(2): 315–332.CrossRefGoogle Scholar
  12. Medhavy T T, Dadhwal V K, Parihar J S, Ruhal D S, Khera A P, Jarwal S D and Singh J (1989). Use of single acquisition IRS LISS I digital data for preharvest wheat acreage (1988–89) estimation in Haryana, Scientific Note, RSAM/SAC/CAPE/SN/18/89.Google Scholar
  13. Ruhal D S, Dadhwal V K, Parihar J S, Jarwal S D, Medhavy T T, Singh J and Khera A P (1988). Studies on within crop variability in wheat using Landsat TM data, In Proc. National Symp. Remote Sensing in Rural Development, Nov. 17–19, 1988, Hisar, pp. 199–208.Google Scholar
  14. Ruhal D S, Medhavy T T, Jarwal S D, Dadhwal V K, Khera A P and Parihar J S (1990). District-level wheat acreage estimation in Haryana using IRS LISS I digital data, In Proc. National Symp. Remote Sensing for Agricultural Applications, Dec. 6–8, New Delhi, pp. 250–255.Google Scholar
  15. Shimoda H and Sakata T (1988). Accuracy of land use classification for SPOT image data, SPOT-1 Image Utilisation, Assessment Results, Cepadues Edition, Toulouse, France, pp. 631–636.Google Scholar
  16. Toll D L (1985). Effect of Landsat TM, sensor parameters on land cover classification, Remote Sensing Environ., 17(2): 129–140.CrossRefGoogle Scholar
  17. Williams D L, Irons J R, Markham B L, Nelson R F and Toll D L (1983). Impact of TM sensor characteristics on classification accuracy, In Proc. IGARSS '83, San Francisco, California, pp. PS 1 5.1–5.9.Google Scholar

Copyright information

© Springer-Verlag 1993

Authors and Affiliations

  • T T Medhavy
    • 1
  • Tara Sharma
    • 1
  • R P Dubey
    • 1
  • R S Hooda
    • 2
  • K E Mothikumar
    • 2
  • M Yadav
    • 2
  • M L Manchanda
    • 2
  • D S Ruhal
    • 3
  • A P Khera
    • 3
  • S D Jarwal
    • 3
  1. 1.Space Applications Centre (ISRO)Ahmedabad
  2. 2.Haryana State Remote Sensing CentreHisar
  3. 3.Chaudhary Charan Singh Haryana Agricultural UniversityHisar

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