Using Complete Polarimetric Information in Fuzzy Neural Classification of SAR Image Based on Complex Gaussian Distribution

4. Test Data and Results
The test data used in the study the L-band polarimetric SAR of San Francisco acquired by JPL AIRSAR. The image size is 1024x900 pixels. The land covers were classified into four class; they are ocean, tree (the park and the hill), urban and grass (the playground and ball park). Fig. 2 shows the un-filtered and filtered SAR image of San Francisco. To train the neural network, totally 1600 pixels training set is chosen from location of each class for training. For comparison, four setups using different inputs were devised, as given in Table 1.

Setup	A	B	C	D
Input Channel	Convariance Matrix	HH, HV, VV	Covariance Matrix	Covariance Matrix
Classifier	Minimum distance	FDL	FDL	FDL
Distance Measure	Based on Wishart Distribution	Euclidean distance	Euclidean distance	Based on Wishart Distribution
Fuzzy	no	yes	yes	yes

Table 1 The setup of Test
Setup A and D used Wishart distribution as distance measure, while Setup B and C are to used to test the suitability of Euclidean to covariance matrix containing the polarimetric information. Fig.3 shows the final classification results with four different setups. It is observed that the Euclidean distance, devised in Setup B and C, confuses the FDL due to the ambiguity of the off-diagonal terms in polarimetric covariance matrix,; Setup A performs classification well but lack of the fuzzy information in it; Setup B uses only three linear polarizations (diagonal term in covariance matrix) information for classification, and loses some important information contained in off-diagonal term. Setup D uses the covariance matrix and applies the Wishart Distribution in fuzzy c-means iterations. Among the four setup, Setup D clearly outperforms the other three. From the network learning curves (not shown here) of all setups, it is also indicated that Setup D convergence much faster than the rest of setups. It means that by using the Wishart distribution in FDL, the algorithm could quickly hit the class center, and at the same time speed the learn rate.

5. Conclusion
A classification scheme for fully polarimetric SAR imagery data based on a dynamic fuzzy neural network has been proposed and its effectiveness and efficiency has been demonstrated. Complete polarimetric matrix can be easily formed as an n-tuple vector data as inputs to the network and all polarimetric information are naturally implicitly contained in the network. In conclusion, a fuzzy neural network-based classification method has been successfully developed to take advantage of fully polarimetric SAR.


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(b)

Fig 2: SAR Image of San Francisco (a) Unfiltered (b) Filerted


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Fig 3: The classifications Results (a)setup A, (b)setup B,
(c)setup C, (d)setup D.

Reference

• Bezdek, J. C., 1987. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press. New York
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• Lee, J. S. et al., 2000. Terrain Classification Using Polarimetric SAR Data - An Overview. PIERS2000, Boston, USA.
• Tzeng, Y. C. and K. S. Chen, 1997. A fuzzy neural network for SAR image classification. IEEE Trans. Geoscience and Remote Sensing, 36(2), pp.301-307.