Shipwreck identification with side scan sonar image based on fractal texture
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摘要: 为提高侧扫声呐图像中沉船等目标信息的识别精度和识别效率，根据盒维数、毯维数与多重分形谱的侧扫声呐图像纹理特征提取算法，构建了基于分形纹理特征的Adaboost级联分类器沉船目标识别流程。结合实测侧扫声呐图像数据进行水下沉船识别实验，并与灰度共生矩阵和Tamura纹理特征的识别结果进行对比。研究表明，基于分形纹理特征的识别方法综合考虑了图像全局与局部纹理特征，且不依赖人工选取阈值参数与特征向量，可有效提高目标识别精度和识别效率。Abstract: In order to improve the accuracy and efficiency for recognition of underwater targets, fractal texture features including box dimension, blanket dimension and multifractal spectrum are calculated by texture feature extraction algorithm with side scan sonar images, and the shipwreck identification procedure based on Adaboost cascade classifier is constructed. The shipwreck recognition experiments have been carried out, and the results are compared. Research shows that the recognition method based on fractal texture features comprehensively considers the global and local texture features of the image, and does not rely on manual selection of threshold parameters and feature vectors, which can improve the accuracy and efficiency of target recognition.
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图 2 基于分形纹理特征的Adaboost目标识别流程
Figure 2. Adaboost target recognition procedure based on fractal texture features
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图 3 目标识别中的正样本与负样本
左为正样本示例[27]，右为负样本示例。
Figure 3. Positive and negative samples in target recognition
Left is positive sample, right is negative sample.
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图 4 盒维数计算
左为正样本结果，右为负样本结果。
Figure 4. Box dimension calculation
Left is positive sample result, right is negative sample result.
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图 5 不同毯子厚度的分类结果比较
Figure 5. Comparison of classification results of different blanket thicknesses
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图 6 不同样本的多重分形谱结果
左为正样本，右为负样本。
Figure 6. Multifractal spectrum of different samples
Left is positive samples, right is negative samples. Horizontal axis α is singularity index, and vertical axis f（α）is fractal dimension.
表 1 沉船及非沉船目标的多重分形谱参数
Table 1 Parameters of multifractal spectrum of shipwrecked and non-wrecked targets
目标 αmin αmax fmin fmax Δα Δf 沉船1 1.81 2.96 0.07 2.00 1.15 1.93 沉船2 1.92 2.21 1.68 2.00 0.29 0.32 沉船3 1.90 2.35 1.13 2.00 0.45 0.87 非沉船1 1.99 2.04 1.75 2.00 0.05 0.25 非沉船2 1.98 2.05 1.75 2.00 0.07 0.25 非沉船3 1.96 2.03 1.80 2.00 0.07 0.20 注：①αmin和αmax分别代表了图像测度集的最小概率和最大概率，其差值Δα表明图像在概率测度分布中的差异程度，Δα越大则图像各测度区域和分形层次的区别越大，多重分形性质越明显；Δα越小则图像各测度区域和分形层次的区别越小，多重分形性质越微弱。
②fmin和fmax分布代表了图像测度集的最大值和最小值，其差值Δf表明图像在图像测度子集纹理复杂程度上的差异，Δf差值越大则表明图像不同测度子集纹理区别越明显。
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表 2 分形纹理特征识别结果
Table 2 Recognition of fractal texture feature
识别方法 精确度/% 召回率/% F1/% 盒维数 50 78.95 61.2 毯维数 88.2 78.9 83.3 多重分形谱 95 100 97.4
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表 3 多重分形谱、GLCM、Tamura三种纹理特征识别结果
Table 3 Recognition results of multifractal spectrum, GLCM and Tamura
识别方法 精确率/% 召回率/% F1/% 多重分形谱 95 100 97.4 GLCM（d=10） 100 94.7 97.2 Tamura六特征值 94.1 84.2 88.9
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