侧扫声呐图像小目标探测的数据降维方法
Side-Scan Sonar Image Small Target Detection Method Based on Data Dimension Reduction
DOI: 10.12677/AMS.2018.52009, PDF,    国家自然科学基金支持
作者: 姚谦祥, 王 晓, 张雨生, 孟庆利, 朱陈香：淮海工学院测绘与海洋信息学院，江苏 连云港
关键词: 侧扫声呐；目标探测；数据降维；扩散映射；Side Scan Sonar； Target Detection； Data Dimension Reduction； Diffusion Map
摘要: 随着侧扫声呐图像分辨率的不断提高，其小目标探测能力逐渐提高。针对传统侧扫声呐图像目标探测方法大都需提供样本图像，基于统计模型需参数估计，这都属于监督方法的缺陷，提出一种侧扫声呐图像小目标的非监督探测方法。首先，对图像基于扩散映射进行数据降维；其次，直接基于降维之后的图像数据，利用扩散距离定义目标得分方程，通过目标得分方程计算目标得分；最后，根据目标得分，实现小目标探测。通过含不同目标，不同背景下的侧扫声呐图像的探测实验，验证了方法的有效性。
Abstract: With the continuous improvement of the resolution of Side Scan Sonar (SSS) image, its small target detection capability has gradually increased. For the problems of some of the traditional SSS image target detection methods need to provide sample images, and the methods based on statistical model need parameter estimation, which are supervision method, an unsupervised target detection method for SSS image is proposed. Firstly, the image is reduced in dimension by diffusion map; secondly, based on the data after dimension reduction, the target score is calculated by the target scoring equation which is defined by diffusion coordinate; finally, based on the target score, the target detection is achieved. The validity of the method was verified through the detection exper-iments of SSS images with different targets.
文章引用：姚谦祥, 王晓, 张雨生, 孟庆利, 朱陈香. 侧扫声呐图像小目标探测的数据降维方法[J]. 海洋科学前沿, 2018, 5(2): 72-79. https://doi.org/10.12677/AMS.2018.52009

参考文献

[1] Blondel, P. (2009) The Handbook of Side Scan Sonar. Springer, UK.
[Google Scholar] [CrossRef] 
[2] Davy, C.M. and Fenton, M.B. (2013) Technical Note: Side-Scan Sonar Enables Rapid Detection of Aquatic Reptiles in Turbid Lotic Systems. European Journal of Wildlife Research, 59, 123-127.
[Google Scholar] [CrossRef] 
[3] Powers, J., Brewer, S.K., Long, J.M. and Campbell, T. (2015) Evaluating the Use of Side-Scan Sonar for Detecting Freshwater Mussel Beds in Turbid River Environments. Hydrobiologia, 743, 127-137.
[Google Scholar] [CrossRef] 
[4] Fakiris, E., Papatheodorou, G., Geraga, M. and Ferentinos, G. (2016) An Automatic Target Detection Algorithm for Swath Sonar Backscatter Imagery, Using Image Texture and Independent Component Analysis. Remote Sensing, 8, 1-13.
[Google Scholar] [CrossRef] 
[5] Dobeck, G.J. and Hyland, J.C. (1997) Automated Detection and Classification of Sea Mines in Sonar Imagery. AeroSense, International Society for Optics and Photonics, Panama City, 90-110.
[6] Dura, E., Zhang, Y., Liao, X., Dobeck, G.J. and Carin, L. (2005) Active Learning for Detection of Mine-Like Objects in Side-Scan Sonar Imagery. IEEE Journal of Oceanic Engineering, 30, 360-371.
[Google Scholar] [CrossRef] 
[7] Grasso, R. and Spina, F. (2006) Small Bottom Object Density Analysis from Side Scan Sonar Data by a Mathematical Morphology Detector. 9th IEEE International Conference in Information Fusion, Florence, 10-13 July 2006, 1-8.
[Google Scholar] [CrossRef] 
[8] 田晓东, 刘忠. 基于灰度分布模型的声呐图像目标检测算法[J]. 系统工程与电子技术, 2007, 29(5): 695-698.
[9] 梁旭姣, 程永强, 周超, 等. 基于灰度分布信息的鲟鱼声呐图像分析研究[J]. 渔业现代化, 2014, 41(6): 32-35.
[10] 李娟娟, 马硕, 朱枫, 等. 基于主动轮廓的声呐图像水雷识别方法[J]. 计算机应用研究, 2014, 31(12): 3841-3844.
[11] 赵小红. 基于Duffing方程的强混响下弱信号检测[D]: [硕士学位论文]. 哈尔滨: 哈尔滨工程大学, 2012.
[12] 库安邦, 周兴华, 李冠泽. 不同型号侧扫声呐分辨能力和扫宽能力的比较分析[J]. 测绘地理信息, 2018(1): 1-5.
[13] klein公司网站[EB/OL]. http://www.l-3mps.com/Klein/sidescansonar.aspx
[14] KongsBerg公司网站[EB/OL]. http://www.km.kongsberg.com/
[15] 魏莱, 王守觉. 基于流形距离的半监督判别分析[J]. 软件学报, 2010, 21(10): 2445-2453.
[16] Rabin, N. and Coifman, R.R. (2012) Heterogeneous Datasets Representation and Learning Using Diffusion Maps and Laplacian Pyramids. In SDM, 189-199.
[17] Mishne, G. and Cohen, I. (2013) Multiscale Anomaly Detection Using Diffusion Maps. IEEE Journal of Selected Topics in Signal Processing, 7, 111-123.
[Google Scholar] [CrossRef] 
[18] Mishne, G. and Cohen, I. (2014) Multiscale Anomaly Detection Using Diffusion Maps and Saliency Score. IEEE International Conference in Acoustics, Speech and Signal Processing (ICASSP), Florence, 4-9 May 2014, 2823-2827.
[Google Scholar] [CrossRef] 

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