GSCCTL: a general semi-supervised scene classification method for remote sensing images based on clustering and transfer learning

Recently, much research has shown that deep learning methods are superior in scene classification for remote sensing images (HSIs). However, the lack of labelled samples and computational resources present serious obstacles for HSI scene classification. To address these problems, we consider the pragmatic deep learning problem with only a few or no labelled samples. A general semi-supervised scene classification method based on clustering and transfer learning named GSCCTL is proposed to transfer outstanding supervised learning models to semi-supervised scene classification. Specifically, the GSCCTL model iterates between (1) scene clustering and (2) transfer learning to improve scene classification performance for the HSI data set. Since the clustering results may contain noise, we propose a new method to calculate the distance between the samples and the clustering center and add a reliable sample selection operation to the data after clustering. Initially, the model's scene classification performance is weak, and transfer learning is carried out on a few samples close to the clustering center. In subsequent iterations, the model is constantly optimized, more and more samples are selected as reliable samples for training, and the model's scene classification performance is strengthened. Through iterative clustering and transfer learning, the model is progressively optimized and gradually converged until the end condition is reached. Extensive experiments on the UCMerced, AID, and NWPU-RESISC45 remote sensing data sets demonstrate that the GSCCTL model based on clustering and transfer learning improved scene classification accuracy with only a few labelled samples and is suitable for semi-supervised scene classification.

Automated summary

A general semi-supervised scene classification method based on clustering and transfer learning is proposed to improve scene classification performance by iteratively optimizing the model.