Hyperspectral image (HSI) open-set classification is critical for HSI classification models deployed in real-world environments, where classifiers must simultaneously classify known classes and reject unknown classes. Recent methods utilize auxiliary unknown classes data to improve classification performance. However, the auxiliary unknown classes data is strongly assumed to be completely separable from known classes and requires labor-intensive annotation. To address this limitation, this paper proposes a novel framework, HOpenCls, to leverage the unlabeled wild data-that is the mixture of known and unknown classes. Such wild data is abundant and can be collected freely during deploying classifiers in their living environments. The key insight is reformulating the open-set HSI classification with unlabeled wild data as a positive-unlabeled (PU) learning problem. Specifically, the multi-label strategy is introduced to bridge the PU learning and open-set HSI classification, and then the proposed gradient contraction and gradient expansion module to make this PU learning problem tractable from the observation of abnormal gradient weights associated with wild data. Extensive experiment results demonstrate that incorporating wild data has the potential to significantly enhance open-set HSI classification in complex real-world scenarios.

翻译：高光谱图像（HSI）开放集分类对于部署在真实环境中的HSI分类模型至关重要，此类分类器必须同时识别已知类别并拒绝未知类别。现有方法通常利用辅助的未知类别数据来提升分类性能。然而，这些方法强烈假设辅助未知类别数据与已知类别完全可分，且需要耗费大量人力进行标注。为克服这一局限，本文提出一种新颖框架HOpenCls，旨在利用未标注的野外数据——即已知类别与未知类别的混合数据。此类野外数据丰富且可在分类器部署于其真实环境时自由采集。其核心思想是将基于未标注野外数据的开放集HSI分类重新表述为正例-无标记（PU）学习问题。具体而言，我们引入多标签策略以连接PU学习与开放集HSI分类，进而提出梯度收缩与梯度扩展模块，通过处理与野外数据相关的异常梯度权重使该PU学习问题可解。大量实验结果表明，在复杂的真实场景中，引入野外数据能够显著提升开放集HSI分类性能。