三维属性直方图的概念与构建及其在海底小目标多波束前视声呐图像精细自动分割中的应用

The automatic recognition of small targets lying on seabed is widely used in target searching, rescuing, treasure hunting, mine hunting, and navigating for autonomous vehicles under the sea. When a multi-beam forward-looking imaging sonar is employed for automatic recognition of small targets lying on seabed, with strong anti-interference ability, precisely and automatically segmenting a sonar image into multi-region is a crucial and underlying step. With strong anti-interference ability, precisely and automatically segmenting an image from a multi-beam forward-looking imaging sonar and small targets lying on seabed into multi-region is a difficult scientific problem in sonar image processing. This project aims to establish the methods for precise and automatic sonar image segmentation based on three-dimensional bound histograms in order to provide an effective solution to the above problem. The project intends to expand the concept of one- (two-) dimensional bound histograms proposed by the applicant and propose the concept of three-dimensional bound histograms and give the definition of three-dimensional bound histograms. The project intends to optimize the parameters in bound sets by means of the BP neural network and establish the intelligent method for construction of three-dimensional bound histograms. The project intends to expand the image segmentation methods based on one- (two-) dimensional bound histograms proposed by the applicant and establish the methods for precise and automatic sonar image segmentation based on three-dimensional bound histograms. In the project, the concept of three-dimensional bound histograms, the intelligent method for construction of three-dimensional bound histograms by means of the BP neural network, and methods for sonar image segmentation based on three-dimensional bound histograms are original.

海底物体搜寻、海底捞救、海底探宝、水雷探测以及智能水下机器人航行避碰等多方面都涉及海底小目标自动识别。将多波束前视成像声呐应用于海底小目标自动识别，图像的多区域、抗干扰、精细自动分割是关键是基础。海底小目标多波束前视声呐图像的多区域、抗干扰、精细自动分割是声呐图像处理领域的科学难题，项目研究旨在建立基于三维属性直方图的声呐图像精细自动分割方法，为这个科学难题提供一种有效的解决方案。项目拟推广申请人前期提出的一（二）维属性直方图概念，形成三维属性直方图的概念，给出三维属性直方图的定义；拟通过BP神经网络优化属性集参数，建立三维属性直方图的智能化构建方法；拟推广申请人前期提出的基于一（二）维属性直方图的分割方法，建立基于三维属性直方图的声呐图像精细自动分割方法。项目提出的三维属性直方图概念、基于BP神经网络的三维属性直方图智能化构建方法、基于三维属性直方图的声呐图像分割方法等均具原创性。

海洋开发事业的迅速发展和水下工程活动以及海洋军事活动的迫切需要，推动了海洋探测技术的迅速发展。作为海洋高新技术，声呐日益成为海洋水下探测的主要手段，而且有时是唯一手段（比如浑水或远距离情况）。海底物体搜寻、海底捞救、海底探宝（比如锰结核等）、海底生物观察与研究、海底施工、水下航道测量和整治、智能水下机器人导航以及海洋军事活动（如探测水雷）等多方面都涉及海底小目标识别。海底小目标多波束前视声呐图像分割是这些领域有重要的应用前景。为了解决海底小目标多波束前视声呐图像分割问题，本项目研究给出三维属性直方图的概念以及三维属性直方图上的声呐图像分割方法。本项目研究提出的分割方法适用于这样的海底小目标声呐图像：图像信混比大于0.9分贝，混噪比大于0.92分贝，目标亮区、目标暗区、混响区尺寸差别不过分悬殊。本项目研究提出的分割方法具有可以实现海底小目标多波束前视声呐图像的多区域自动分割，并具有一定的自学习能力、抗干扰和精细分割能力。本项目提出的三维属性直方图拓展了属性直方图的概念、丰富了图像处理学科中直方图这个重要的基本概念。还有，本项目给出的基于BP神经网络的三维属性直方图的智能化构建方法具有一定的方法论意义。还有，本项目提出的基于三维属性直方图的图像分割方法也可以丰富图像处理学科中图像分割的方法。因此本项目研究在（声呐）图像处理领域具有重要的学术价值和科学意义。