Insufficient prior knowledge of a captured hyperspectral image (HSI) scene may lead the experts or the automatic labeling systems to offer incorrect labels or ambiguous labels (i.e., assigning each training sample to a group of candidate labels, among which only one of them is valid; this is also known as partial label learning) during the labeling process. Accordingly, how to learn from such data with ambiguous labels is a problem of great practical importance. In this paper, we propose a novel superpixelwise low-rank approximation (LRA)-based partial label learning method, namely SLAP, which is the first to take into account partial label learning in HSI classification. SLAP is mainly composed of two phases: disambiguating the training labels and acquiring the predictive model. Specifically, in the first phase, we propose a superpixelwise LRA-based model, preparing the affinity graph for the subsequent label propagation process while extracting the discriminative representation to enhance the following classification task of the second phase. Then to disambiguate the training labels, label propagation propagates the labeling information via the affinity graph of training pixels. In the second phase, we take advantage of the resulting disambiguated training labels and the discriminative representations to enhance the classification performance. The extensive experiments validate the advantage of the proposed SLAP method over state-of-the-art methods.

翻译：在标注过程中，对捕获的高光谱图像（HSI）场景的先验知识不足可能导致专家或自动标注系统提供错误标记或模糊标记（即，将每个训练样本分配至一组候选标记，其中仅有一个标记是有效的；这亦被称为部分标记学习）。因此，如何从这类具有模糊标记的数据中学习是一个具有重要实际意义的问题。本文提出了一种新颖的基于超像素低秩近似（LRA）的部分标记学习方法，命名为SLAP，这是首次在高光谱图像分类中考虑部分标记学习。SLAP主要由两个阶段构成：训练标记去歧义与预测模型获取。具体而言，在第一阶段，我们提出了一种基于超像素LRA的模型，为后续的标记传播过程准备亲和图，同时提取判别性表示以增强第二阶段分类任务的性能。随后，通过训练像素的亲和图，标记传播过程传递标记信息以实现训练标记的去歧义。在第二阶段，我们利用得到的去歧义训练标记与判别性表示来提升分类性能。大量实验验证了所提出的SLAP方法相较于现有先进方法的优势。