Navigating unmanned aerial vehicles in precarious environments is of great importance. It is necessary to rely on alternative information processing techniques to attain spatial information that is required for navigation in such settings. This paper introduces a novel deep learning-based approach for navigating that exclusively relies on synthetic aperture radar (SAR) images. The proposed method utilizes deep neural networks (DNNs) for image matching, retrieval, and registration. To this end, we introduce Deep Cosine Similarity Neural Networks (DCSNNs) for mapping SAR images to a global descriptive feature vector. We also introduce a fine-tuning algorithm for DCSNNs, and DCSNNs are used to generate a database of feature vectors for SAR images that span a geographic area of interest, which, in turn, are compared against a feature vector of an inquiry image. Images similar to the inquiry are retrieved from the database by using a scalable distance measure between the feature vector outputs of DCSNN. Methods for reranking the retrieved SAR images that are used to update position coordinates of an inquiry SAR image by estimating from the best retrieved SAR image are also introduced. Numerical experiments comparing with baselines on the Polarimetric SAR (PolSAR) images are presented.

中文翻译：

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使用深余弦相似神经网络在GPS受限的环境中基于合成孔径雷达图像的定位

在不稳定的环境中驾驶无人飞行器非常重要。必须依靠替代​​信息处理技术来获得在这样的设置中导航所需的空间信息。本文介绍了一种新颖的基于深度学习的导航方法，该方法仅依赖于合成孔径雷达（SAR）图像。所提出的方法利用深度神经网络（DNN）进行图像匹配，检索和配准。为此，我们引入了深度余弦相似神经网络（DCSNN），用于将SAR图像映射到全局描述性特征向量。我们还针对DCSNN引入了一种微调算法，并且DCSNN用于生成跨越感兴趣的地理区域的SAR图像的特征向量数据库，与查询图像的特征向量进行比较。通过使用DCSNN的特征向量输出之间的可伸缩距离度量，可以从数据库中检索与查询类似的图像。还介绍了用于对检索到的SAR图像进行重新排序的方法，该方法用于通过从最佳检索到的SAR图像进行估算来更新查询SAR图像的位置坐标。提出了与极化SAR（PolSAR）图像上的基线进行比较的数值实验。