The large-scale labeled data is very crucial to train a classification model with strong generalization ability. However, the collection of large-scale labeled data is very expensive, especially in the remote sensing fields. The available of labeled data is very limited for the hyperspectal image classification. To address such a challenge, active learning and semi-supervised learning are two popular techniques in machine learning community. In this paper, we integrate active learning and semi-supervised learning into a framework by improving the quality of pseudo-labels for hyperspectral remote sensing images. In the proposed method, the collaboration of the spatial features and spectral features are adopted to improve the ability of classifier. Specifically, we train two classifiers with spatial feature and spectral feature respectively based on the labeled data. Then the prediction probabilities of the two classifiers are combined for strong prediction. With active learning technique, we can select a batch of the most informative samples and obtain a new labeled dataset. Two classifiers based on the new labeled dataset can be obtained. With these two classifiers, another prediction results by combining their predictions can be obtained. To guarantee the quality of the pseudo-labels, the samples that are predicted with the same labels before and after active learning are assigned with pseudo-labels. The samples that can not be assigned with high confident samples are regarded as the candidate pool for active learning. The final predictions are obtained by the classification models trained on the pseudo-labeled samples and the labeled samples with both the spatial features and spectral features. The experiments on two popular hyperspectral images show that the proposed method outperforms the state-of-the-art and baseline methods.

大规模标记数据对于训练泛化能力强的分类模型至关重要。然而，大规模标记数据的收集非常昂贵，尤其是在遥感领域。对于高光谱图像分类，可用的标记数据非常有限。为了应对这样的挑战，主动学习和半监督学习是机器学习社区中的两种流行技术。在本文中，我们通过提高高光谱遥感图像伪标签的质量，将主动学习和半监督学习整合到一个框架中。该方法通过空间特征和光谱特征的协同来提高分类器的能力。具体来说，我们根据标记数据分别训练具有空间特征和光谱特征的两个分类器。然后结合两个分类器的预测概率进行强预测。通过主动学习技术，我们可以选择一批信息量最大的样本并获得新的标记数据集。可以获得基于新标记数据集的两个分类器。使用这两个分类器，可以通过组合它们的预测来获得另一个预测结果。为保证伪标签的质量，对主动学习前后用相同标签预测的样本分配伪标签。不能分配高置信度样本的样本被视为主动学习的候选池。最终的预测是通过对伪标记样本和具有空间特征和光谱特征的标记样本训练的分类模型获得的。在两个流行的高光谱图像上的实验表明，所提出的方法优于最先进的方法和基线方法。