Classification Results
The output error rates computed on the test data sets for each Level-3 pixel-based classifier and "spatial" classifier are listed in Tables 3 and 4 below. The apparent ranking of the pixel-based classifiers (based solely on the classifier error rates computed from the test data sets) from best to worst are NN-MNF13, MLC-CA, MLC-DBFE, NN-5, and MLC-5. However, based on visual inspection of the entire scene, the classification of the entire data set results in a ranking from best to worst is NN-MNF13, MLC-5, MLC-CA, MLC-DBFE and NN-5. Performance of the contextual and region based classifiers ranks them as MRF-MNF13 and MRF-PCA8. The results of each classifier are illustrated in the sections below.
Table 3. Pixel-based Classifier Accuracy
| Vegetation Type |
NN-5 |
MLC-5 |
NN-MNF |
MLC-DBFE |
MLC-LDFE |
| Scrub |
95.3 % |
84.8 % |
97.2 % |
81.3 % |
80.2 % |
| Willow Swamp |
93.8 % |
90.5 % |
96.3 % |
87.2 % |
88.5 % |
| Cabbage Palm Hammock |
87.1 % |
87.9 % |
88.2 % |
82.8 % |
93.8 % |
| CP/Oak Hammock |
63.1 % |
63.1 % |
83.0 % |
53.4 % |
94.4 % |
| Slash Pine |
62.3 % |
67.1 % |
83.0 % |
53.4 % |
94.4 % |
| Oak/Broadleaf Hammock |
46.0 % |
47.2 % |
90.8 % |
98.3 % |
83.8 % |
| Hardwood Swamp |
88.6 % |
94.3 % |
100.0 % |
69.5 % |
89.5 % |
| Overall Uplands |
76.6% |
76.4% |
92.5% |
78.0% |
86.8% |
| Graminoid Marsh |
74.8 % |
74.2 % |
98.6 % |
80.3 % |
79.1 % |
| Spartina Marsh |
87.3 % |
89.6 % |
90.8 % |
77.9 % |
83.5 % |
| Cattail Marsh |
83.6 % |
90.8 % |
94.8 % |
75.7 % |
82.2 % |
| Salt Marsh |
97.1 % |
96.9 % |
99.3 % |
88.1 % |
87.4 % |
| Mud Flats |
92.8 % |
73.6 % |
83.8 % |
79.9 % |
82.7 % |
| Overall Wetlands |
87.1% |
85.0% |
93.5% |
80.4% |
83.0% |
Table 4. Contextual-based Classifier Accuracy
| Vegetation Type |
MRF-PC8 |
MRF-MNF13 |
| Scrub |
93.3 % |
93.4 % |
| Willow Swamp |
90.1 % |
93.0 % |
| Cabbage Palm Hammock |
89.5 % |
93.4 % |
| CP/Oak Hammock |
74.6 % |
83.7 % |
| Slash Pine |
94.4 % |
78.3 % |
| Oak/Broadleaf Hammock |
96.9 % |
92.6 % |
| Hardwood Swamp |
96.2 % |
98.1 % |
| Overall Uplands |
96.2 % |
98.1 % |
| Graminoid Marsh |
81.9 % |
82.6 % |
| Spartina Marsh |
91.5 % |
87.3 % |
| Cattail Marsh |
95.0 % |
95.0 % |
| Salt Marsh |
98.3 % |
95.2 % |
| Mud Flats |
79.5 % |
79.7 % |
| Overall Wetlands |
89.2 % |
87.9 % |
Output Images from tested classifiers
Pixel-based Classifiers
Contextual Region-based Classifiers
Discussion of Results
Overall, the contextual-based classifiers seemed to do a better job of classification than the pixel-based classifiers. The Markov Random Field classifier using the MNF input data most accurately classified the overall image and had an upland accuracy of 90.3% and a wetland accuracy of 87.9%. The MRF-MNF13 result seemed to accurately map the distribution of scrub as well as correctly identify many of the upland groupings. The wetland vegetation (both within and outside the impoundments) was also mapped well with the only exception being an over-classification of salt marsh at the top of the image.
The MRF-PCA8 also performed well in classifying the entire image, but the amount of scrub lying to the north of the road was underestimated. The MRF-PCA8 also misclassified willow swamp and cabbage palm hammock in impoundment T24-D as slash pine. However, this input combination did better than the MRF-MNF13 for mapping salt marsh. Both the MRF-MNF13 and the MNF-PCA8 provided good overall classifications, but the MRF-MNF13 was better overall and would require less post-processing to import the result into a GIS system.
Of the pixel-based classifiers, the NN using the MNF data performed the well with an upland accuracy of 92.5% and a wetland accuracy of 93.5% based on training data. Although results of the classifier applied to the entire image were quite good, some of the wetlands were classified to have too much salt marsh. Since both the contextual and the pixel-based classifiers performed well using the MNF data as inputs, it is assumed that this transformation can be used successfully for the classification of hyperspectral imagery.
The Maximum Likelihood classifier using the linear discriminant analysis (Canonical Analysis) actually yielded good results for the entire data set, although the error accuracy for the uplands is 86.3% and a wetlands is 83.0%. LDFE was able to separate the upland classes well, with the only notable exception being an over-classification of oak hammock. However, this method was able to accurately identify several small regions of slash pine which other classifiers incorrectly labeled. Unfortunately, however, this classifier misclassified Spartina marsh as salt marsh in many of the impoundments (T10-F through T10-I).
The ML classifier had an upland accuracy of 76.4% and a wetland accuracy of 85.0%. However, visual evaluation of the classification results for the entire data set looked much better than the error rates computed on the test data sets. While the ML classifier performed well on the full data set, it had difficulties in identifying slash pine communities and misclassified cabbage palm hammock in isolated regions of the impoundments.
Using the decision boundary feature extraction method, the ML classifier overall had accuracy of 78.0% for uplands and 80.5% for wetlands, and the overall results did not look as good as results from other classification methods. The MLC-DBFE overclassified salt marsh in both the T10-D through T10-I impoundments as well as in impoundment T24-D and it underclassified scrub.
The NN-5 classifier yielded an accuracy similar to those obtained by the ML classifier for the test data sets, but visual inspection of the full data set showed that this classification was not as good as the other classifier/inputs. Analysis of the full data set showed that the NN-5 classifier had difficulty in the identification of slash pine communities, misclassification of some portions of spartina marsh and misclassification of isolated cabbage palm hammocks in the impoundments.
Last Modified: Wed Apr 14, 1999
CSR/TSGC Team Web
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