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Multisensor Classification of Wetland Environments using Airborne Multispectral and SAR Data
Classification Results
An ensemble based approach was adopted for classification of the test site data. Data from each sensor were classified separately, then single-sensor classifier outputs were combined at the sensor integration stage.
Single Sensor Classification
Single-sensor classifiers based on radial basis function (RBF) neural networks and multisensor integrated classifiers based on ensemble approaches to combining classifiers were used for the classification of the CAMS Optical, CAMS Thermal, and AIRSAR data sets. These results are shown in Table 1.
Table 1. Single Sensor Classification Results
CAMS Optical
93.2% |
CAMS Thermal
55.8% |
AIRSAR
74.1% |
Overall, CAMS Optical performed the best of three sensors, with the only difficulty coming in misclassifying 8% of the low proximal marsh as water. Given the amount of water in the low marshes, this is not surprising. AIRSAR classified water and trees reasonably well, but had trouble separating both the high proximal marsh from the high distal marsh, as well as, separating the spoil/barren flats from the three marsh types. The similar moisture content and vegetation geometry in the high proximal marsh and high distal marshes are likely the cause of this result. The CAMS Thermal sensor had trouble separating water and high proximal marsh, separating low proximal marsh and high distal marsh, and performed poorly for trees.
Multisensor Classification
A simple average and simple product of the single-sensor classifier results were computed with equal weights for each sensor, 95.2% and 94.9% overall classification rate respectively. Both performed better than the best single-sensor classifier, CAMS Optical, indicating the potential increase in performance through combining classifiers for different sensors, even with naive rules. Comparing the multisensor integration techniques, sensor weighted sum and product rules performed better than their equally weighted versions, demonstrating the need for utilizing sensor reliability measures into the classification scheme. Of these sensor integrated results, in addition to the RBF network integrator, all produced comparable results.
Table 2. Multisensor Classification Results
Simple Average
95.2% |
Sensor Weighted Average
95.8% |
Multisensor Joint Classifier
96.0% |
The best overall classification rate was obtained from the joint classification of the three sensors using an RBF network based on a mixture of Gaussians distribution for each class. While the percent increase was not sizable, it shows that some information was lost in classifying each sensor separately; that by combining the three sensors into a single classifier input vector, the CAMS Thermal and AIRSAR were able to provide useful information to the classification of the CAMS Optical data set. However, in general, flexibility is lost in classification of a combined data set in terms of the potential use of statistical classification techniques in conjunction with fusion of results via neural networks.
Last Modified: Wed Apr 14, 1999
CSR/TSGC Team Web
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