Submeter high-resolution remote sensing image land cover classification could provide significant help for urban monitoring, management, and planning. Deep learning (DL)-based models have achieved remarkable performance in many land cover classification tasks through end-to-end supervised learning. However, the excellent performance of DL-based models relies heavily on a large number of well-annotated samples, which is impossible in practical land cover classification scenarios. Additionally, the training set could contain all of the different land cover types. To overcome these problems, in this article, a semisupervised multiple-CNN ensemble learning method, namely semi-MCNN, is proposed to solve the land cover classification problem. Considering the lack of labeled samples, a semisupervised learning strategy was adopted to leverage large amounts of unlabeled data. In the proposed approach, an automatic sample selection method called an ensembled teacher model dataset generation was adopted to select samples and generate a dataset from large amounts of unlabeled data automatically. To tackle the error propagation problem, an important strategy was adopted to correct the errors by pretraining on the selected unlabeled data, and fine-tuning on the labeled data. Moreover, the semisupervised idea together with the multi-CNN ensemble framework was integrated into an end-to-end architecture. This could significantly improve the generalization ability of the semisupervised model, as well as the classification accuracy. Experiments were conducted on Shenzhen's land cover data and two other public remote sensing datasets. These experiments confirmed the superior performance of the proposed semi-MCNN compared to the state-of-the-art land cover classification models.

中文翻译：

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Semi-MCNN：一种使用亚米级 HRRS 图像进行城市土地覆盖分类的半监督多 CNN 集成学习方法

亚米级高分辨率遥感影像土地覆盖分类可为城市监测、管理和规划提供重要帮助。通过端到端的监督学习，基于深度学习 (DL) 的模型在许多土地覆盖分类任务中取得了卓越的性能。然而，基于深度学习的模型的优异性能在很大程度上依赖于大量标注良好的样本，这在实际的土地覆盖分类场景中是不可能的。此外，训练集可以包含所有不同的土地覆盖类型。为了克服这些问题，本文提出了一种半监督多CNN集成学习方法，即semi-MCNN来解决土地覆盖分类问题。考虑到标记样本的缺乏，采用半监督学习策略来利用大量未标记的数据。在所提出的方法中，采用称为集成教师模型数据集生成的自动样本选择方法来选择样本并自动从大量未标记数据中生成数据集。为了解决错误传播问题，采用了一种重要的策略，通过对选定的未标记数据进行预训练，并对标记数据进行微调来纠正错误。此外，将半监督思想与多 CNN 集成框架集成到端到端架构中。这可以显着提高半监督模型的泛化能力，以及分类精度。对深圳的土地覆盖数据和另外两个公共遥感数据集进行了实验。