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Poster Session 2
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A Study of Euclidean classifier
Accuracy assessment
Accuracy assessment was carried out using contingency tables (confusion matrices) generated from the comparisons between the test data and classified images. Approximate accuracies were then determined by calculating the percentage of pixels correctly classified along the row of the matrix, the calculating similar percentage for
columns of the matrix and taking the average of these values.
Euclidean distance classifier was used to classify the test area and the output was written on a magnetic tape. The classification was performed by using the training sets for 8 classes. The output displayed is shown in
Fig. 2. That is Maximum likelihood classifier is shown in
fig3. The raw data of the test area is shown in
Fig 1. The Result are given in Table 1,2 and 3.
Fig. 1 Raw Data
Fig. 2 Classified output
(Euclidean)
Fig. 3 Classified output
(Maximum likelihood)
Results and conclusion
The classification was performed with Euclidean and Maximum-likelihood classifiers. The results obtained with these schemes were used to compare the classification software and to conclude this study. The time taken for classification and the accuracy attained with the classification scheme are the important view points of any user. The amount of information needed is also a measure o compare the classification software.
Euclidean classifier takes very lesser time when compared to the Maximum likelihood estimation, still the accuracy attained with this method is encouraging. From the table it is clear that the Maximum-likelihood classifier is relatively slow because of the classification of a data sample requires the evaluation of the decision function for each class being considered. The size of the data set is immaterial to the process since each data point is classified independently.
Table. 3
Clasification Accuracy
and rates
| S.No. |
Classifier | No. of Pixels | Time taken (seconds) |
Percentage accuracy
(training set) |
Percentage accuracy
(Field verification) |
| l. |
Euclidean |
49,000 |
720 |
93.00 |
60 |
| 2. |
Maximum Likelihood | 49,000 |
2400 | 97.92 | 75 |
References
- Eppler, "Applied Multivariate Analysis", Academic Press, New York 1975.
- Fisher, R.A. "The se of multiple measurements in taxonomic problems", 1936.
- Hogg & Gaig, "Introduction to Mathematical Statistics", Macmillan Pub., New York, 1970.
- King Sun Fu, "Pattern Recognition in Remote Sensing", 9th Proc. University of Felonies, 1971.
- Maruthachalam, M. "familiarization course on Digital Image Processing of Remotely Sensed Data". Institute of Remote Sensing, Anna Univ., madras, 1985.
- Morrison, "Statistical Data Analysis", Macmillan Pub. New York, 1970.
- Snain and Davis (Eds.). "Remote Sensing-A quantitative approach, 1978.
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