In this letter, we introduce deep active learning (AL) for multi-label classification (MLC) problems in remote sensing (RS). In particular, we investigate the effectiveness of several AL query functions for MLC of RS images. Unlike the existing AL query functions (which are defined for single-label classification or semantic segmentation problems), each query function in this paper is based on the evaluation of two criteria: i) multi-label uncertainty; and ii) multi-label diversity. The multi-label uncertainty criterion is associated to the confidence of the deep neural networks (DNNs) in correctly assigning multi-labels to each image. To assess this criterion, we investigate three strategies: i) learning multi-label loss ordering; ii) measuring temporal discrepancy of multi-label predictions; and iii) measuring magnitude of approximated gradient embeddings. The multi-label diversity criterion is associated to the selection of a set of images that are as diverse as possible to each other that prevents redundancy among them. To assess this criterion, we exploit a clustering based strategy. We combine each of the above-mentioned uncertainty strategies with the clustering based diversity strategy, resulting in three different query functions. All the considered query functions are introduced for the first time in the framework of MLC problems in RS. Experimental results obtained on two benchmark archives show that these query functions result in the selection of a highly informative set of samples at each iteration of the AL process.

翻译：本文提出将深度主动学习（AL）应用于遥感（RS）中的多标签分类（MLC）问题。具体而言，我们研究了多种主动学习查询函数在遥感图像多标签分类中的有效性。与现有的针对单标签分类或语义分割问题定义的主动学习查询函数不同，本文中的每个查询函数均基于两个标准的评估：i）多标签不确定性；ii）多标签多样性。多标签不确定性标准与深度神经网络（DNNs）正确为每幅图像分配多标签的置信度相关。为评估该标准，我们探讨了三种策略：i）学习多标签损失排序；ii）衡量多标签预测的时间差异性；iii）衡量近似梯度嵌入的幅度。多标签多样性标准与选取一组尽可能彼此多样化的图像相关，以防止冗余。为评估该标准，我们采用了一种基于聚类的策略。我们将上述每种不确定性策略与基于聚类的多样性策略相结合，形成了三种不同的查询函数。所有考虑的查询函数均为首次在遥感多标签分类问题的框架中引入。在两个基准数据集上获得的实验结果表明，这些查询函数能够在主动学习过程的每次迭代中选择高度信息丰富的样本集。