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首页> 外文会议>2011 IEEE International Conference on Systems, Man, and Cybernetics >Autocorrelation features for synthetic aperture sonar image seabed segmentation
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Autocorrelation features for synthetic aperture sonar image seabed segmentation

机译：合成孔径声纳图像海底分割的自相关特征

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High-resolution synthetic aperture sonar (SAS) systems yield richly detailed images of seabed environments. Algorithms that automatically segment and label seabed textures such as coral, sea grass, sand ripple, and mud, require suitable features that discriminate between the texture classes. Here we present a robust, parameterized SAS image texture model based on the autocorrelation function (ACF) of the intensity image. This ACF texture model has been shown to accurately model first- and second-order statistical features of various seabed environments. An unsupervised multi-class k-means segmentation algorithm that uses the features derived from the ACF model is employed to label rock and ripple textures from a set of textured SAS images. The results of the segmentation are compared against the performance of the segmentation approach using biorthogonal wavelets and Haralick features. In the described experiments, the ACF model features are shown to produce better segmentations than the features based on wavelet coefficients and Haralick features for classifiers of low complexity.
机译：高分辨率合成孔径声纳（SAS）系统产生丰富的海底环境的详细图像。自动段和标签海底纹理如珊瑚，海草，砂纹纹，泥浆等算法需要合适的特征，可以区分纹理类。在这里，我们呈现了一种基于强度图像的自相关函数（ACF）的强大的参数化SAS纹理模型。此ACF纹理模型已被证明可以准确地模拟各种海底环境的第一和二阶统计特征。使用从ACF模型导出的功能的无监督的多级K均值分割算法用于标记来自一组纹理的SAS图像的摇滚和纹波纹理。将分割结果与使用双正交小波和Haralick特征进行分割方法的性能进行比较。在所描述的实验中，ACF模型特征被示出了比基于小波系数和Haralick特征的特征产生更好的分割，以及用于低复杂度的分类器的特征。

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