Target detetction in Clutter for sonar imagery

声纳图像杂波中的目标检测

• 批准号: EP/H012354/1
• 负责人: Yvan Petillot
• 金额: $ 15.42万
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FH012354%2F1
• 关键词: Target; detetction; Clutter; sonar; imagery

This proposal aims at studying new techniques for detection and classification of targets underwater using 3D and texture analysis. On simple seabed types such as flat sand, it is very easy to detect and classify targets. It becomes much more difficult when the seabed is either highly cluttered with rocky or coral structures, marine life such as seaweed or is of a complex nature (large rocky outcrops and sand dunes). In those areas, classical target detection and classification techniques fails as they tend to concentrate on the shape of the target, classically recovered using shadow analysis (the acoustic shadow is casted by the target on the seabed). On the other hand, the analysis of the target echo is difficult for classical high resolution sonars as they are susceptible to speckle noise and in general not resolved enough for classification. Detection and classification in such challenging scenarios can be improved by detectiing the targets as an outlier in the current texture field. This can be done using 2D or 3D texture measures but as most strong textures are due to the 3D nature of the seabed, we believe that 3D texture analysis will be more effective and therefore propose to focus on these. Classification can be addressed with the development of new higher resolution sonars (SAS) and new 3D sonars (Interferometric SAS / Side Scan). As resolution increases, the structure of the echo will become more apparent and techniques developed in the machine vision and pattern recognition communities can be used. This is the secondary objective of this proposal.

该建议旨在研究利用三维和纹理分析进行水下目标检测和分类的新技术。在简单的海底类型，如平坦的沙滩上，很容易探测和分类目标。当海底布满岩石或珊瑚结构、海藻等海洋生物或具有复杂性质（大型岩石露头和沙丘）时，这就变得更加困难。在这些地区，经典的目标检测和分类技术会失败，因为它们往往集中在目标的形状上，经典地使用阴影分析（声影是由海底目标投射的）来恢复。另一方面，目标回波的分析对于经典的高分辨率声纳来说是困难的，因为它们容易受到斑点噪声的影响，并且通常不足以进行分类。通过将目标检测为当前纹理场中的离群值，可以改进在这种具有挑战性的场景中的检测和分类。这可以使用2D或3D纹理测量来完成，但由于大多数强纹理是由于海底的3D性质，我们认为3D纹理分析将更有效，因此建议重点关注这些。分类可以通过开发新的更高分辨率声纳（SAS）和新的三维声纳（干涉SAS /侧扫描）来解决。随着分辨率的提高，回声的结构将变得更加明显，并且可以使用机器视觉和模式识别社区中开发的技术。这是本提案的第二个目标。

项目成果

Target detection using statistical MIMO

使用统计 MIMO 进行目标检测

• 发表时间: 2013
• 期刊: Proceedings of Meetings on Acoustics
• 作者: Pailhas, Y

High-Resolution Sonars: What Resolution Do We Need for Target Recognition?

• DOI: 10.1155/2010/205095
• 发表时间: 2010-02
• 期刊: EURASIP Journal on Advances in Signal Processing
• 影响因子: 1.9
• 作者: Y. Pailhas;Y. Pétillot;C. Capus

Design of artificial landmarks for underwater simultaneous localisation and mapping

水下同步定位与建图人工地标设计

• DOI: 10.1049/iet-rsn.2011.0103
• 发表时间: 2013
• 期刊: IET Radar, Sonar & Navigation
• 作者: Pailhas Y

Proceedings of the 2012 International Conference on Detection and Classification of Underwater Targets

2012年水下目标探测与分类国际会议论文集

• 发表时间: 2014
• 作者: Quidu Isabelle

Cascade of boosted classifiers for automatic target recognition in synthetic aperture sonar imagery

用于合成孔径声纳图像中自动目标识别的增强分类器级联

• DOI: 10.1121/1.4788639
• 发表时间: 2013
• 作者: Sawas J