Abu Avi, Diamant Roee
IEEE Trans Image Process. 2019 Jul 29. doi: 10.1109/TIP.2019.2930148.
The recent boost in undersea operations has led to the development of high-resolution sonar systems mounted on autonomous vehicles. These vehicles are used to scan the seafloor in search of different objects such as sunken ships, archaeological sites, and submerged mines. An important part of the detection operation is the segmentation of sonar images, where the object's highlight and shadow are distinguished from the seabed background. In this work, we focus on the automatic segmentation of sonar images. We present our enhanced fuzzybased with Kernel metric (EnFK) algorithm for the segmentation of sonar images which, in an attempt to improve segmentation accuracy, introduces two new fuzzy terms of local spatial and statistical information. Our algorithm includes a preliminary de-noising algorithm which, together with the original image, feeds into the segmentation procedure to avoid trapping to local minima and to improve convergence. The result is a segmentation procedure that specifically suits the intensity inhomogeneity and the complex seabed texture of sonar images. We tested our approach using simulated images, real sonar images, and sonar images that we created in two different sea experiments, using multibeam sonar and synthetic aperture sonar. The results show accurate segmentation performance that is far beyond the stateof-the-art results.
近期海底作业的增加促使了安装在自主航行器上的高分辨率声纳系统的发展。这些航行器用于扫描海底，寻找诸如沉船、考古遗址和水下地雷等不同物体。检测操作的一个重要部分是声纳图像的分割，即从海底背景中区分出物体的高光和阴影。在这项工作中，我们专注于声纳图像的自动分割。我们提出了用于声纳图像分割的基于核度量的增强模糊（EnFK）算法，该算法为提高分割精度引入了局部空间和统计信息这两个新的模糊项。我们的算法包括一个初步的去噪算法，它与原始图像一起输入到分割过程中，以避免陷入局部最小值并提高收敛性。结果是一种特别适合声纳图像强度不均匀性和复杂海底纹理的分割方法。我们使用模拟图像、真实声纳图像以及在两个不同的海上实验中利用多波束声纳和合成孔径声纳创建的声纳图像对我们的方法进行了测试。结果显示出精确的分割性能，远远超过了现有技术的结果。
