Semi-Supervised Land Cover Classification of Remote Sensing Imagery Using CycleGAN and EfficientNet

Image classification of very high resolution (VHR) images is a fundamental task in the remote sensing domain for various applications, such as land cover mapping, vegetation mapping, and urban planning. Recently, deep learning-based semantic segmentation networks demonstrated the promising performance for pixel-level image classification. However, deep learning-based approaches are generally limited by the requirement of a sufficient amount of labeled data to obtain stable accuracy, and acquiring reference labels of remotely-sensed VHR images is very labor-extensive and expensive. Hence, this paper applied a semi-supervised learning-based CycleGAN and EfficientNet for VHR remote sensing image classification to overcome this problem. The proposed method achieved the highest accuracy than the other benchmarks. The largest increase in accuracy was observed in a test site containing complex objects due to the regularization effect of the semi-supervised method using unlabeled data. Moreover, results indicated that a relatively sufficient amount of unlabeled data compared with labeled data are required to increase the classification accuracy by controlling the amount of labeled and unlabeled data. Finally, we verified that the semi-supervised method returned significantly improved results irrespective of the three classification network structures, displaying the applicability of the method for semi-supervised image classification on remotely-sensed VHR images.

Automated summary

This paper applies a semi-supervised learning-based CycleGAN and EfficientNet for VHR remote sensing image classification to overcome the limitation of requiring a large amount of labeled data. The proposed method achieved the highest accuracy among other benchmarks, especially in test areas with complex objects. Results also showed that a sufficient amount of unlabeled data is required to improve classification accuracy.