Semi-MCNN: A Semisupervised Multi-CNN Ensemble Learning Method for Urban Land Cover Classification Using Submeter HRRS Images

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2020, Vol.13, p.4973-4987
• Main Authors: Fan, Runyu, Feng, Ruyi, Wang, Lizhe, Yan, Jining, Zhang, Xiaohan
• Format: Article
• Language: English
• Subjects: Classification; Data; Datasets; Deep learning (DL); Ensemble learning; Error correction; Gallium nitride; Image classification; Image resolution; Land cover; land cover classification; Machine learning; Model accuracy; Remote sensing; semisupervised; Semisupervised learning; Task analysis; Teaching methods; Training; Urban areas
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2020.3019410

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.