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基于非监督特征学习的侧扫声呐图像聚类分割研究

发布时间:2019-03-28 13:11
【摘要】:侧扫声呐是海洋活动中常用的探测装置,它通过成像的方式展示信息。侧扫声呐图像具有分辨率低、噪声大、灰度分布范围窄的特点,也因此给侧扫声呐图像的目标分割带来很大困难。在分析当前主流的图像分割技术与发展趋势后,本文对通用性强的聚类算法和基于非监督特征学习的方法做了研究。首先,针对侧扫声呐噪声强的特点,分析了声呐噪声产生的原因,声呐噪声的分类并对噪声进行建模。在此基础上分析了声呐去噪的常用方法,将应用在光学图像上的若干效果突出的算法在声呐图像上进行了尝试。对具有不同场景特点的声呐图像做了去噪实验。实验结果表明,上述方法对于声呐图像也是有效的。在此基础上,进一步分析了算法之间去噪效果差异和不同噪声去噪效果之间的优劣原因,并描述了这些去噪算法在声呐去噪应用上的一些可能的改进措施。接着,结合侧扫声呐图像的特点,遴选纹理特征中的两种目前应用广泛的重要特征,局部二值模式和类哈尔特征,对它们的原理做了详细的描述,并利用目前在诸多领域取得了突破性进展的深度学习算法中的一种,稀疏自编码器,来对侧扫声呐图像进行特征学习,成功地构建了专门针对侧扫声呐图像的特征。通过对比分析认为使用特征学习得到的特征对基于聚类的侧扫声呐图像分割具有明显的优势。然后,详细地介绍了 K均值聚类、层次聚类、模糊聚类和谱聚类这四种能够在侧扫声呐图像分割中应用的常用的基础聚类算法,讨论了它们的原理,并使用本文通过非监督学习得到的特征,用图像的灰度信息作为对照组,在侧扫声呐图像的样本中进行了试验,并对结果做了比较分析。最后,在基于前述K均值算法取得的良好聚类分割效果的基础上,针对数据量大算法运行耗时的问题,使用基于OpenMP和CUDA的两种并行化算法对K均值聚类算法进行加速,并通过实验对比说明侧扫声呐图像的聚类分割速度得到提高。
[Abstract]:Side scan sonar is a common detection device in ocean activities. It displays information by imaging. Side-scan sonar image has the characteristics of low resolution, large noise and narrow gray-scale distribution. Therefore, it is very difficult to segment the target of side-scan sonar image. After analyzing the current mainstream image segmentation technology and the development trend, this paper studies the universal clustering algorithm and the unsupervised feature learning method. Firstly, according to the strong noise characteristics of side-scan sonar, the causes of sonar noise are analyzed, and the classification of sonar noise and the modeling of sonar noise are carried out. On this basis, the common methods of sonar de-noising are analyzed, and some prominent algorithms applied to optical images are tried on sonar images. The denoising experiments of sonar images with different scene characteristics are carried out. The experimental results show that the proposed method is also effective for sonar images. On this basis, the difference of denoising effect among algorithms and the advantages and disadvantages of different noise denoising effects are further analyzed, and some possible improvement measures of these denoising algorithms in sonar de-noising applications are described. Then, combining the characteristics of side-scan sonar images, two important features, local binary pattern and quasi-Hal feature, which are widely used in texture features, are selected, and their principles are described in detail. One of the deep learning algorithms, sparse self-encoder, which has made a breakthrough in many fields at present, is used to study the features of side-scan sonar images, and the features of side-scan sonar images are constructed successfully. Through comparative analysis, it is concluded that the features obtained from feature learning have obvious advantages in the segmentation of side-scan sonar images based on clustering. Then, K-means clustering, hierarchical clustering, fuzzy clustering and spectral clustering are introduced in detail, which can be used in the segmentation of side-scan sonar images, and their principles are discussed. Using the features obtained by unsupervised learning and using the gray-scale information of the image as the control group, the experiments were carried out in the samples of side-scan sonar images, and the results were compared and analyzed. Finally, two parallel algorithms based on OpenMP and CUDA are used to accelerate the K-means clustering algorithm, which is based on the good segmentation results obtained by the K-means algorithm and the time-consuming running of the algorithm with large amount of data, which is based on the above-mentioned K-means algorithm. The experimental results show that the clustering segmentation speed of side scan sonar images is improved.
【学位授予单位】:哈尔滨工程大学
【学位级别】:硕士
【学位授予年份】:2016
【分类号】:TP391.41

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