Deep learning has not been routinely employed for semantic segmentation of seabed environment for synthetic aperture sonar (SAS) imagery due to the implicit need of abundant training data such methods necessitate. Abundant training data, specifically pixel-level labels for all images, is usually not available for SAS imagery due to the complex logistics (e.g., diver survey, chase boat, precision position information) needed for obtaining accurate ground-truth. Many hand-crafted feature based algorithms have been proposed to segment SAS in an unsupervised fashion. However, there is still room for improvement as the feature extraction step of these methods is fixed. In this work, we present a new iterative unsupervised algorithm for learning deep features for SAS image segmentation. Our proposed algorithm alternates between clustering superpixels and updating the parameters of a convolutional neural network (CNN) so that the feature extraction for image segmentation can be optimized. We demonstrate the efficacy of our method on a realistic benchmark dataset. Our results show that the performance of our proposed method is considerably better than current state-of-the-art methods in SAS image segmentation.

中文翻译：

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迭代、深度和无监督合成孔径声纳图像分割

由于隐含需要大量训练数据，因此深度学习尚未常规用于合成孔径声纳 (SAS) 图像的海底环境语义分割。由于获得准确的地面实况所需的复杂后勤工作（例如，潜水员调查、追逐船、精确位置信息），SAS 图像通常无法获得丰富的训练数据，特别是所有图像的像素级标签。已经提出了许多基于手工制作的特征的算法来以无监督的方式分割 SAS。但是，由于这些方法的特征提取步骤是固定的，因此仍有改进的空间。在这项工作中，我们提出了一种新的迭代无监督算法，用于学习 SAS 图像分割的深度特征。我们提出的算法在聚类超像素和更新卷积神经网络 (CNN) 的参数之间交替进行，以便优化图像分割的特征提取。我们在现实的基准数据集上证明了我们的方法的有效性。我们的结果表明，我们提出的方法的性能明显优于当前 SAS 图像分割中最先进的方法。