Hashing methods have recently been shown to be very effective in the retrieval of remote sensing (RS) images due to their computational efficiency and fast search speed. Common hashing methods in RS are based on hand-crafted features on top of which they learn a hash function, which provides the final binary codes. However, these features are not optimized for the final task (i.e., retrieval using binary codes). On the other hand, modern deep neural networks (DNNs) have shown an impressive success in learning optimized features for a specific task in an end-to-end fashion. Unfortunately, typical RS data sets are composed of only a small number of labeled samples, which make the training (or fine-tuning) of big DNNs problematic and prone to overfitting. To address this problem, in this letter, we introduce a metric-learning-based hashing network, which: 1) implicitly uses a big, pretrained DNN as an intermediate representation step without the need of retraining or fine-tuning; 2) learns a semantic-based metric space where the features are optimized for the target retrieval task; and 3) computes compact binary hash codes for fast search. Experiments carried out on two RS benchmarks highlight that the proposed network significantly improves the retrieval performance under the same retrieval time when compared to the state-of-the-art hashing methods in RS.

中文翻译：

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基于度量学习的深度哈希网络，用于基于内容的遥感图像检索

由于其计算效率和快速搜索速度，散列方法最近被证明在检索遥感 (RS) 图像方面非常有效。RS 中常见的散列方法基于手工制作的特征，在此基础上他们学习散列函数，该函数提供最终的二进制代码。然而，这些特征并未针对最终任务（即使用二进制代码进行检索）进行优化。另一方面，现代深度神经网络 (DNN) 在以端到端的方式为特定任务学习优化特征方面取得了令人瞩目的成功。不幸的是，典型的 RS 数据集仅由少量标记样本组成，这使得大型 DNN 的训练（或微调）存在问题并且容易过度拟合。为了解决这个问题，在这封信中，我们介绍了一个基于度量学习的哈希网络，其中：1) 隐式地使用一个大的、预训练的 DNN 作为中间表示步骤，无需重新训练或微调；2) 学习一个基于语义的度量空间，其中的特征针对目标检索任务进行了优化；3) 计算紧凑的二进制哈希码以进行快速搜索。在两个 RS 基准上进行的实验表明，与 RS 中最先进的散列方法相比，所提出的网络在相同检索时间下显着提高了检索性能。