In recent years, deep neural networks (DNNs) have been found very successful for multi-label classification (MLC) of remote sensing (RS) images. Self-supervised pre-training combined with fine-tuning on a randomly selected small training set has become a popular approach to minimize annotation efforts of data-demanding DNNs. However, fine-tuning on a small and biased training set may limit model performance. To address this issue, we investigate the effectiveness of the joint use of self-supervised pre-training with active learning (AL). The considered AL strategy aims at guiding the MLC fine-tuning of a self-supervised model by selecting informative training samples to annotate in an iterative manner. Experimental results show the effectiveness of applying AL-guided fine-tuning (particularly for the case where strong class-imbalance is present in MLC problems) compared to the application of fine-tuning using a randomly constructed small training set.

翻译：近年来，深度神经网络在遥感图像多标签分类任务中取得了显著成功。自监督预训练结合随机选取小规模训练集的微调策略，已成为减少数据需求型深度神经网络标注负担的主流方法。然而，在小型且存在偏倚的训练集上进行微调可能限制模型性能。针对该问题，本文研究了自监督预训练与主动学习联合使用的有效性。所提出的主动学习策略通过迭代式选取信息量丰富的训练样本进行标注，旨在引导自监督模型的多标签分类微调过程。实验结果表明，与使用随机构建的小型训练集进行微调相比，主动学习引导的微调方法（尤其在多标签分类问题中存在严重类别不平衡时）具有显著有效性。