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| Citation: | 
|
Semi-supervised PolSAR Image Classification Based on the Neighborhood Minimum Spanning Tree
DOI: 10.12000/JR18104
More Information-
Abstract
In this paper, a novel semi-supervised classification method based on the Neighborhood Minimum Spanning Tree (NMST) is proposed to solve the Polarimetric Synthetic Aperture Radar (PolSAR) terrain classification when labeled samples are few. Combining the idea of self-training method and spatial information of the pixels in PolSAR image, a new help-training sample selection strategy based on spatial neighborhood information is proposed, named as NMST, to select the high reliable unlabeled samples to enlarge the training set and improve the base classifier. Finally, the PolSAR image is classified by this improved classifier. The experiments results tested on three PolSAR data sets show that the proposed method achieves a better performance than existing classification methods when the number of labeled samples is few. -
References
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Li Wan-chun, Huang Cheng-feng. Optimal Trajectory Analysis for the Receiver of Passive Location Systems Using Direction Of Arrival and Doppler Measurements[J]. Journal of Radars, 2014, 3(6): 660-665. doi: 10.12000/JR14118
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- Figure 1. Self-training method
- Figure 2. The gray value of 9 elements in PolSAR covariance matrix
- Figure 3. Weighted undirected graph G and its minimum spanning tree
- Figure 4. The spanning process of neighborhood minimum spanning tree
- Figure 5. Semi-supervised PolSAR classification based on the neighborhood minimum spanning tree
- Figure 6. Classification results of the Flevoland data acquired by AIRSAR
- Figure 7. Classification result of the Flevoland data acquired by Radarsat-2
- Figure 8. Classification result of the San Francisco data acquired by Radarsat-2
- Figure 9. The effects of number of iterations in the proposed method