Deep metric learning (DML) based methods have been found very effective for content-based image retrieval (CBIR) in remote sensing (RS). For accurately learning the model parameters of deep neural networks, most of the DML methods require a high number of annotated training images, which can be costly to gather. To address this problem, in this paper we present an annotation cost efficient active learning (AL) method (denoted as ANNEAL). The proposed method aims to iteratively enrich the training set by annotating the most informative image pairs as similar or dissimilar, while accurately modelling a deep metric space. This is achieved by two consecutive steps. In the first step the pairwise image similarity is modelled based on the available training set. Then, in the second step the most uncertain and diverse (i.e., informative) image pairs are selected to be annotated. Unlike the existing AL methods for CBIR, at each AL iteration of ANNEAL a human expert is asked to annotate the most informative image pairs as similar/dissimilar. This significantly reduces the annotation cost compared to annotating images with land-use/land cover class labels. Experimental results show the effectiveness of our method. The code of ANNEAL is publicly available at https://git.tu-berlin.de/rsim/ANNEAL.

翻译：基于深度度量学习（DML）的方法已被发现在遥感（RS）领域中的基于内容图像检索（CBIR）任务中非常有效。为了准确学习深度神经网络的模型参数，大多数DML方法需要大量标注的训练图像，而收集这些图像可能成本高昂。为解决这一问题，本文提出一种标注成本高效的主动学习方法（记为ANNEAL）。该方法旨在通过迭代地将最具信息量的图像对标注为相似或不相似来丰富训练集，同时精确建模深度度量空间。这通过两个连续步骤实现：第一步基于现有训练集建模图像对相似性；第二步选择最不确定且多样（即信息量最丰富）的图像对进行标注。与现有用于CBIR的主动学习方法不同，在ANNEAL的每次主动学习迭代中，人类专家被要求将最具信息量的图像对标注为相似/不相似。相较于使用土地利用/土地覆盖类别标签标注图像，这大幅降低了标注成本。实验结果表明了该方法的有效性。ANNEAL的代码公开于https://git.tu-berlin.de/rsim/ANNEAL。