董凌宇, 单瑞, 刘慧敏, 于得水, 杜凯. 基于分形纹理特征的侧扫声呐图像沉船识别方法研究[J]. 海洋地质与第四纪地质, 2021, 41(4): 232-239. DOI: 10.16562/j.cnki.0256-1492.2020070301
引用本文: 董凌宇, 单瑞, 刘慧敏, 于得水, 杜凯. 基于分形纹理特征的侧扫声呐图像沉船识别方法研究[J]. 海洋地质与第四纪地质, 2021, 41(4): 232-239. DOI: 10.16562/j.cnki.0256-1492.2020070301
DONG Lingyu, SHAN Rui, LIU Huimin, YU Deshui, DU Kai. Shipwreck identification with side scan sonar image based on fractal texture[J]. Marine Geology & Quaternary Geology, 2021, 41(4): 232-239. DOI: 10.16562/j.cnki.0256-1492.2020070301
Citation: DONG Lingyu, SHAN Rui, LIU Huimin, YU Deshui, DU Kai. Shipwreck identification with side scan sonar image based on fractal texture[J]. Marine Geology & Quaternary Geology, 2021, 41(4): 232-239. DOI: 10.16562/j.cnki.0256-1492.2020070301

基于分形纹理特征的侧扫声呐图像沉船识别方法研究

Shipwreck identification with side scan sonar image based on fractal texture

  • 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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