Semantic segmentation has received wide attention as a feasible solution to effectively interpret the information in remote sensing images. Solutions are typically built with a static closed-set perception, where all labels are known a priori. However, in real-world applications, such as remote sensing images, one has to handle objects from unknown classes. Open-set semantic segmentation (OSSS) is an approach that incorporates open-set perception into semantic segmentation, allowing the recognition of unknown classes of objects. Different studies have explored the use of OSSS in remote sensing images. However, their performance is limited due to the poor and overlapped representation of the features extracted from images. This results in an embedding space with low discrimination among the classes. This article introduces a novel loss function called prototypical triplet loss, which uses prototype representation and metric learning to improve open-set recognition. In addition, two open-set classifiers, one based on principal components and the other on prototypical distance, were also proposed once they took advantage of the features obtained by the prototypical triplet loss. Experiments were done with two public remote sensing image datasets: Vaihingen and Potsdam. The results demonstrate that the proposed methods improve OSSS compared to other state-of-the-art approaches. These results reinforce the importance of this type of approach, enabling applications in real systems that require open-set recognition. All codes are freely available at https://github.com/Brilhador/tgrs2023 to foster further research in this area.

中文翻译：

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遥感图像开集语义分割的原型度量学习方法


语义分割作为有效解释遥感图像信息的可行解决方案受到了广泛关注。解决方案通常是用静态闭集感知构建的，其中所有标签都是先验已知的。然而，在现实应用中，例如遥感图像，必须处理未知类别的对象。开放集语义分割（OSSS）是一种将开放集感知融入语义分割的方法，允许识别未知类别的对象。不同的研究探索了 OSSS 在遥感图像中的使用。然而，由于从图像中提取的特征表示较差且重叠，它们的性能受到限制。这导致了类之间歧视性较低的嵌入空间。本文介绍了一种称为原型三元组损失的新型损失函数，它使用原型表示和度量学习来改进开放集识别。此外，利用原型三元组损失获得的特征，还提出了两种开放集分类器，一种基于主成分，另一种基于原型距离。使用两个公共遥感图像数据集进行了实验：Vaihingen 和 Potsdam。结果表明，与其他最先进的方法相比，所提出的方法改进了 OSSS。这些结果强化了此类方法的重要性，使得能够在需要开放集识别的实际系统中应用。所有代码均可在 https://github.com/Brilhador/tgrs2023 免费获取，以促进该领域的进一步研究。