Semisupervised learning (SSL) techniques explore the progressive discovery of the hidden latent data structure by propagating supervised information on unlabeled data, which are thereafter used to reinforce learning. These schemes are beneficial in remote sensing, where thousands of new images are added every day, and manual labeling results are prohibitive. Our work introduces an ensemble-based semisupervised deep learning approach that initially takes a subset of labeled data Dl, which represents the latent structure of the data and progressively propagates labels automatically from an expanding set of unlabeled data Du. The ensemble is a set of classifiers whose predictions are collated to derive a consolidated prediction. Only those data having a high-confidence prediction are considered as newly generated labels. The proposed approach was exhaustively validated on four public datasets, achieving appreciable results compared to the state-of-the-art methods in most of the evaluated configurations. For all datasets, the proposed approach achieved a classification F1-score and recall of up to 90%, on average. The SSL and recursive scheme also demonstrated an average gain of ∼2 % at the last training stage in such large datasets.

中文翻译：

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基于集成的遥感半监督学习方法

半监督学习 (SSL) 技术通过在未标记数据上传播监督信息来探索隐藏的潜在数据结构的渐进发现，然后将这些信息用于强化学习。这些方案在遥感中是有益的，因为每天都会添加数千张新图像，而手动标记的结果令人望而却步。我们的工作引入了一种基于集成的半监督深度学习方法，该方法最初采用标记数据 Dl 的子集，该子集表示数据的潜在结构，并从一组扩展的未标记数据 Du 中自动逐步传播标签。集成是一组分类器，其预测被整理以得出综合预测。只有那些具有高置信度预测的数据才被视为新生成的标签。所提出的方法在四个公共数据集上得到了详尽的验证，与大多数评估配置中的最新方法相比，取得了可观的结果。对于所有数据集，所提出的方法平均实现了高达 90% 的分类 F1 分数和召回率。在如此大的数据集的最后一个训练阶段，SSL 和递归方案也表现出约 2% 的平均增益。