|
|
|
Segment based classification of Indian urban environment
V. Results
A. Thresholding and Segmentation
Out of the three methods implemented for finding threshold values, segmentation results using threshold values derived from Otsu method are visually better than other method used for the study. Threshold values for multispectral bands were found to be 13,11,10 respectively for band 1, 2 and 3. Segmentation of multispectral bands was carried out with respective threshold values.
B. Refinement of Segmented images
Once the initial segmented image was obtained, it was refined so that smaller segments were reduced by merging them to bigger segments. After 203 iterations of merging process, segments got stabilized.
C. Classification of Segmented images
The overall classification accuracy and the accuracy for most of the classes were significantly higher for segment based classification. Further, results of per-segment classification reveal that the per- segment neural classification provides slightly higher accuracy than per-segment GMLC. Use of GLCM texture feature Mean in the per-segment neural classification also provides higher, but statistically insignificantly different accuracy than the per-segment neural classification with pure spectral features.
VI. Conclusions
- Otsu method of finding threshold provides better segmentation results compared to other two methods considered for the study.
- The per-segment GMLC provides significantly higher test accuracy than the per-pixel GMLC.
- The per-segment ANN classification provides slightly higher, though statistically insignificantly different, test accuracy than the per-segment GMLC.
- Use of Standard Deviation values of segments as texture information reduces test accuracy in the per-segment ANN Classification.
- Use of GLCM texture feature Mean, derived from spectral features improves test accuracy in the per-segment ANN Classification though it is statistically insignificantly different.
References
- G. Johannsen and J. Bille, “A threshold Selection method using Information measures”, Proceedings of the 6th International Conference on Pattern Recognition, Munich, Germany, pp. 140-143, 1982.
- N. Otsu, “A threshold selection method from grey-level histograms”, IEEE Trans. on systems, Man, and Cybernetics, SMC-8, No.1, pp. 62-66, 1979.
- H.J. Trussel, Comments on “Picture Thresholding Using an Iterative Selection Method”, IEEE Trans. on systems, Man, and Cybernetics, SMC-9, No. 5, p. 311, 1979.
- J.R. Beveridge, J. Griffith, R.R. Kohler, A.R. Hanson and E.M. Riseman, “Segmenting Images using localised histograms and region merging”, International Journal of Computer Vision, vol. 2, pp. 311-347, 1989.
- R. M. Haralick, K. Shanmugam and I. Dinstien,” Textural features for image classification”, IEEE Trans. on Systems, Man, and Cybernetics, SMC-3, pp. 610-621,1973.
- R. G. Congalton, R.G. Oderwald and R.A. Mead, “Assessing Landsat Classification Accuracy Using Discrete Multivariate Analysis Statistical Techniques”, Photogrammetric Engineering and Remote Sensing, vol. 49, pp.1671-1678, 1983.
|
|
|