Minimum Spanning Tree Segmentation and Extract with Image Edge Weight Optimization
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摘要: 针对无监督图像分割方法对噪声敏感而导致图像建模困难、分割结果准确率低等问题，该文提出一种图像边缘权重优化的最小生成树分割提取方法。首先，利用L0梯度最小值平滑处理噪声再结合Otsu优化Canny边缘检测，得到更加准确的边缘信息；其次，重新设计权重函数，采用更加合理的色差空间构建加权图，通过改进分割准则优化物体合并与区分过程；最后，选择不同类型图片进行抗噪性、分割效果实验。实验结果表明：相对于其他算法，该文算法的抗噪性能优秀，分割精度平均提升5.15%，过分割率平均下降32.07%，欠分割率平均下降2.69%。将其运用在实际航空遥感图像的河道湖泊提取中，所得结果相比其他主流算法结构更加完整，无关信息更少，抗噪性能更好。Abstract: The unsupervised image segmentation method is sensitive to noise, leading to difficult building image model and poor accuracy of segmentation results. In this paper, a minimum spanning tree segmentation and extract with image edge weight optimization is proposed. Firstly, L0 gradient minimum is used to smooth the noise. The Canny edge detection with Otsu is optimized to obtain more accurate edge information. Secondly, the weight function is redesigned and the weighted graph by using more reasonable color difference space is constructed. The segmentation criterion is improved to optimize the process of object merging and distinguishing. Finally, different types of images are chosen to conduct experiments with noise resistance and segmentation effect. Experimental comparing results show that the proposed algorithm has excellent anti-noise performance, and the segmentation accuracy is improved by 5.15% on average, the over-segmentation rate is decreased by 32.07% on average, and the under-segmentation rate is decreased by 2.69% on average. Moreover, this method is applied to the river and lake extraction of aviation and remote sensing images, and the result has more complete structure, less irrelevant information and better anti-noise performance.
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Key words:
- Image segmentation /
- Image edge /
- Minimum spanning tree /
- Image extraction /
- Airborne remote sensing
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表 1 合成图像分割结果峰值信噪比/平均结构相似性 (dB / %)
噪声比例 FH方法 RSSFCA SFFCM AFCF 本文方法 高斯5% 18.21 / 81.96 26.48 / 94.59 26.04 / 95.77 13.78 / 84.17 27.63 / 96.61 高斯10% 20.71 / 87.06 21.06 / 68.20 21.68 / 94.33 13.67 / 83.82 23.72 / 94.97 高斯15% 15.67 / 79.36 17.90 / 41.14 19.07 / 92.48 13.41 / 83.27 21.79 / 94.11 乘性5% 23.54 / 85.72 24.57 / 74.67 32.55 / 97.84 32.49 / 97.56 33.23 / 97.89 乘性10% 22.84 / 84.76 20.44 / 51.69 32.57 / 97.24 20.20 / 94.70 32.76 / 97.43 乘性15% 19.33 / 80.71 19.87 / 48.79 32.44 / 96.91 22.62 / 92.84 28.18 / 97.23 泊松 21.31 / 82.03 25.08 / 82.21 27.92 / 92.83 28.49 / 93.76 34.84 / 96.94 
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