Analysis of Window Size and Classification Accuracy using Spectral and Textural Information from JERS-1 SAR Satellite Image

Because of the difficulties in identifying specific details in the combined images, the training site assignments were done by taking the training results from images who have spectral information only (Figure 5). This can only be done if both of the images are guaranteed to have the same datum dan coordinate system.

As for the accuracy assessment of classification result of combined spectral features and seven images from texture extraction, we used reference data from year 2003 Quickbird image. Even tough the reference data, taken in the year 2003, and the JERS-1 SAR image, which taken in the year 1997, have a significant difference in record year, this can be anticipated by using a sample point only in the areas that are assumed not have significant changes. This can be seen in Figure 6.



Figure 6. Accuracy assessment of classification results


From the combined images with different sizes, sample points were taken by stratified random sampling. From the four existing classes, we took 10 sample points from each class, so we have a total of 40 sample point and every class will have the same chance to be taken in this accuracy assessment. The advantage of the stratified random sampling method is that all kind of land will be included in the sample, no matter small it is.
From the accuracy assessment we obtain results as follows:
  • For window size 3×3, overall accuracy is 37.50% and the kappa index is 0.338.
  • For window size 5×5, overall accuracy is 47.50% and the kappa index is 0.441.
  • For window size 7×7, overall accuracy is 62.50% and the kappa index is 0.596.
  • For window size 9×9, overall accuracy is 77.50% and the kappa index is 0.753.
  • For window size 11×11, overall accuracy is 80.00% and the kappa index is 0.780.
  • For window size 13×13, overall accuracy is 75.00% and the kappa index is 0.723.
  • For window size 15×15, overall accuracy is 62.50% and the kappa index is 0.588
Graphics that describe the relations between window size and classification accuracy are depicted in Figure 7 and Figure 8.



Figure 7. Relations between window sizes with kappa index




Figure 8. Relations between window sizes with overall accuracy


From Figure 7 and 8, the kappa index and overall accuracy in window size of 3×3 through 11×11 is increase, but decrease in window size of 13×13 and 15×15. From this fact we can inferred that increasing the window size in the extraction process can only increases kappa index and overall accuracy to a certain limit, that is on window size 11×11. It is also can be concluded that increasing the window size does not provide significant contribution to increase the accuracy of the classification using combination of spectral and textural features. From this accuracy assessment, we also obtained that the best window size for image classification is 11×11 with 80.00% overall accuracy and kappa index 0.780.

CONCLUSIONS
From this study it can be concluded that:

  1. Increasing the window size during the texture extraction stage does not give significant contribution in improving the classification accuracy.
  2. From the seven window size that has been evaluated, the best window size for classification is 11×11 with 80.00% overall accuracy and kappa index 0.780. Window size 7×7, 9×9, 13×13, and 15×15, are still usable from classification since the overall accuracy is above 50.00%

References

  • Baraldi, A., and Parmiggiani, F. (1995). An Investigation of the Textural Characteristics Associated with Gray Level Co-occurrence Matrix Statistical Parameters. IEEE Transactions on Geoscince and Remote Sensing volume 33
  • Haralick, R. M., Shanmugam, K., and Dinstein, I. (1973), Textural Features for Image Classification.IEEE Trans. Sys. Manage. Cybernetics, SMC-3(6), 610-621
  • Lillesand, T. M. and Kiefer, R. W. (2000). Remote Sensing and Image Interpretation. (4th Edition). Jhon Wiley and Sons, New York
  • Wikantika, K., Uchida, S., and Yamamoto, Y., (2001), Discrimination of Vegetable Field in Mountainous Area with Spectral and Textural Information Derived from Landsat-ETM, Proceedings of the International Symposium on Land Use-Land Cover Changes Contribution to Asia Environmental Problems, Tokyo, Japan
  • Wikantika, K., Uchida, S., & Yamamoto, Y. (2004). An Evaluation of The Use of Integrated Spectral and Texture Features to Identify Agricultural Land Cover Types in Pangalengan, West Java, Indoensia. Japan Agricultural Research Quarterly, Vol. 38, No. 2., 137-148

Window 3x3 Window 5x5
Window 7x7 Window 9x9
Window 11x11 Window 13x13
Window 15x15
Page 2 of 2
Previous