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摘要: 针对传统的极化SAR(PolSAR)图像超像素分割算法中采用的距离度量对相似性表征能力不足的问题，该文提出了一种基于测地线距离的极化SAR图像快速超像素分割算法。首先，对图像进行正六边形初始化与不稳定点初始化；其次，利用实对称Kennaugh矩阵之间的测地线距离来度量当前不稳定点与其搜索范围内其他聚类中心点之间的相似度，以便更准确地为当前不稳定点分配标签，从而快速减少不稳定点的数量；最后，利用后处理步骤消除孤立像素点以生成最终的超像素。利用仿真极化SAR数据验证了初始化方法的有效性和测地线距离度量的高效性，并利用仿真和实测数据将该文算法与其他4种算法进行对比分析。实验结果表明，该文方法生成的超像素具有更规则的形状并且能够准确地贴合真实地物边缘，同时具有更高的运算效率。
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关键词:
- 极化SAR图像 /
- 超像素分割 /
- Kennaugh矩阵 /
- 测地线距离 /
- 正六边形
Abstract: Considering the lack of similarity capabilities of the distance metric used in the traditional Polarimetric Synthetic Aperture Radar (PolSAR) image superpixel segmentation algorithm, a novel PolSAR image superpixel segmentation algorithm based on geodesic distance is proposed in this paper. First, the PolSAR image is initialized as a hexagonal distribution, and all pixels are initialized as unstable pixels. Thereafter, the geodesic distance between two real symmetric Kennaugh matrices is used to measure the similarity between the current unstable point and another cluster point in the search region to more accurately assign labels to unstable points, thereby effectively reducing the number of unstable points. Finally, the postprocessing procedure is used to remove small, isolated regions and generate the final superpixels. To verify the effectiveness of the initialization method and the high efficiency of the geodesic distance, extensive experiments are conducted using simulated PolSAR images. Moreover, the proposed algorithm is analyzed and compared with four other algorithms using simulated and real-world images. Experimental results show that the superpixels generated using the proposed method exhibit higher computational efficiency and a more regular shape that can more accurately fit the edges of real objects compared with those using the four other algorithms.-
Key words:
- PolSAR image /
- Superpixel segmentation /
- Kennaugh matrix /
- Geodesic distance /
- Hexagon
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图 3 两种初始化方法的示意图
Figure 3. The sketch map of unstable pixels initialization of two methods
图 5 Flevoland实测极化SAR数据的Pauli-RGB图像
Figure 5. The Pauli-RGB image of the Flevoland real-world PolSAR data
图 8 FHAWS算法与FHAGS算法在S=10时的实验结果图
Figure 8. The results of FHAWS method and FHAGS method with S=10
表 1 5种算法基于区域A的4种评价度量
Table 1. Four criteria of five methods for the region A of the real-world PolSAR image
算法 BR USE ASA 运行时间(s) POL-SLIC 0.6958 0.2558 0.9600 58 POL-LSC 0.5872 0.2276 0.9593 5 POL-IER 0.7762 0.2268 0.9602 32 FHAWS 0.7868 0.2097 0.9611 33 FHAGS 0.7321 0.2415 0.9596 26 
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表 2 针对实测极化SAR数据的5种超像素分割算法的运行时间
Table 2. Running Time(s) of five superpixel generation methods for the real-world PolSAR image
算法 运行时间(s) 分割后处理时间(s) 总时间(s) POL-SLIC 839 124 963 POL-LSC 371 – 371 POL-IER 641 65 706 FHAWS 618 66 684 FHAGS 523 64 587
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