Remote sensing through semantic segmentation of satellite images contributes to the understanding and utilisation of the earth's surface. For this purpose, semantic segmentation networks are typically trained on large sets of labelled satellite images. However, obtaining expert labels for these images is costly. Therefore, we propose to rely on a low-cost approach, e.g. crowdsourcing or pretrained networks, to label the images in the first step. Since these initial labels are partially erroneous, we use active learning strategies to cost-efficiently refine the labels in the second step. We evaluate the active learning strategies using satellite images of Bengaluru in India, labelled with land cover and land use labels. Our experimental results suggest that an active label refinement to improve the semantic segmentation network's performance is beneficial.

翻译：通过对卫星图像进行语义分割的遥感技术，有助于理解和利用地球表面。为此，语义分割网络通常需要在大规模标记的卫星图像数据集上进行训练。然而，获取这些图像的专家标签成本高昂。因此，我们提出首先采用低成本的标注方法（例如众包或预训练网络）对图像进行初步标记。由于这些初始标签存在部分错误，我们在第二步中利用主动学习策略以经济高效的方式对标签进行精化。我们使用印度班加罗尔的卫星图像（标注了土地覆盖和土地利用类别）对主动学习策略进行评估。实验结果表明，通过主动标签精化提升语义分割网络性能是具有显著效益的。