The sonar image segmentation is needed such as in underwater object orientation and recognition, in collision prevention and navigation of underwater robots, in underwater investigation and rescue, in seafloor object seeking, in seafloor salvage, and in marine military affairs like torpedo detection. The wavelet-based methods have the ability of multiscale and multiresolution, and they are apt at edge detection and feature extraction of images. The applications of these methods to the sonar image segmentation are increasingly raised. The contents of the article are to classify the sonar image segmentation methods with wavelets and to describe main ideas, advantages, disadvantages, and conditions of use of every method. In the methods for sonar image region (or texture) segmentation, the thought of multiscale (or multiresolution) analysis of the wavelet transform is usually combined with other theories or methods such as the clustering algorithms, the Markov random field, co-occurrence matrix, Bayesian theory, and support vector machine. In the methods for sonar image edge detection, the space–frequency local characteristics of the wavelet transform are usually utilized. The wavelet packet-based and beyond wavelet-based methods can usually reach more precise segmentation. The article also gives 12 directions (or development trends predicted) of the sonar image segmentation methods with wavelets which should be researched deeply in the future. The aim of writing this review is to make the researchers engaged in sonar image segmentation learn about the research works in the field in a short time. Up to now, the similar reviews in this field have not been found.

中文翻译：

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声纳图像分割的小波方法综述

声纳图像分割在水下目标定位与识别、水下机器人防撞与导航、水下调查与救援、海底寻物、海底打捞、鱼雷探测等海上军事等领域都需要进行声纳图像分割。基于小波的方法具有多尺度、多分辨率的能力，适用于图像的边缘检测和特征提取。这些方法在声纳图像分割中的应用越来越多。文章的内容是对小波声纳图像分割方法进行分类，并描述每种方法的主要思想、优缺点和使用条件。在声纳图像区域（或纹理）分割方法中，小波变换的多尺度（或多分辨率）分析思想通常与聚类算法、马尔可夫随机场、共生矩阵、贝叶斯理论、支持向量机等其他理论或方法相结合。在声纳图像边缘检测方法中，通常利用小波变换的空频局部特性。基于小波包和超越小波包的方法通常可以达到更精确的分割。文章还给出了未来应深入研究的小波声纳图像分割方法的12个方向（或预测的发展趋势）。写这篇综述的目的是让从事声纳图像分割的研究人员在短时间内了解该领域的研究工作。到现在，