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PolSAR Image Classification Method Based on Markov Discriminant Spectral Clustering
doi: 10.12000/JR19059
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Abstract
Due to the existing spectral clustering methods have low accuracy for PolSAR image classification, a Markov-based Discriminative Spectral Clustering(MDSC) method is proposed, which has the characteristics of low-rank and sparse decomposition. Firstly, a real low-rank probability transfer matrix is restored as an input to the standard Markov spectral clustering method to reduce the influence of noise on the classification result. Then the discriminative information is introduced into the objective function to make the polarimetric SAR image data can be more fully used. Finally, the augmented Lagrangian multiplier method is used to solve the objective function optimization problem under low-rank and probability simplex constraints. Experiments on three different data sets of Flevoland, Oberpfaffenhofen, and Xi’an show that our method has good accuracy and low sensitivity, which having a good classification performance. -
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References
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- Figure 1. Real probability transfer matrix construction profile
- Figure 2. Algorithm frame diagram
- Figure 3. Pseudo-color map, class diagram and classification results of different algorithms for farmland maps in the Flevoland region of the Netherlands
- Figure 4. Pseudo-color map, class diagram and data classification results of different algorithms in the Oberpfaffenhofen region of Germany
- Figure 5. Pseudo-color map, class diagram and data classification results of different algorithms in Xi’an area
- Figure 6. Pseudo-color map, class diagram and classification results of different algorithms for large farmland data in the Flevoland region of the Netherlands
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Figure 7. Classification results of different
$\lambda $ $\beta $ -
Figure 10. Classification results of different regular item parameter
$\xi $ -
Figure 8. Classification results for different
$\lambda $ $\beta $ -
Figure 9. Classification results of different
$\lambda $ $\beta $