Self-Supervised Pre-Training of Transformers for Satellite Image Time Series Classification
preprintposted on 2020-11-02, 21:49 authored by Yuan YuanYuan Yuan, Lei Lin
Satellite image time series (SITS) classification is a major research topic in remote sensing and is relevant for a wide range of applications. Deep learning approaches have been commonly employed for SITS classification and have provided state-of-the-art performance. However, deep learning methods suffer from overfitting when labeled data is scarce. To address this problem, we propose a novel self-supervised pre-training scheme to initialize a Transformer-based network by utilizing large-scale unlabeled data. In detail, the model is asked to predict randomly contaminated observations given an entire time series of a pixel. The main idea of our proposal is to leverage the inherent temporal structure of satellite time series to learn general-purpose spectral-temporal representations related to land cover semantics. Once pre-training is completed, the pre-trained network can be further adapted to various SITS classification tasks by fine-tuning all the model parameters on small-scale task-related labeled data. In this way, the general knowledge and representations about SITS can be transferred to a label-scarce task, thereby improving the generalization performance of the model as well as reducing the risk of overfitting. Comprehensive experiments have been carried out on three benchmark datasets over large study areas. Experimental results demonstrate the effectiveness of the proposed method, leading to a classification accuracy increment up to 2.38% to 5.27%. The code and the pre-trained model will be available at https://github.com/linlei1214/SITS-BERT upon publication.
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
Natural Science Foundation of Jiangsu Province under Grant BK20170897
National Natural Science Foundation of China under Grant 41901356
Research Project of Surveying Mapping and Geoinformation of Jiangsu Province under Grant JSCHKY201905
Environmental Protection Research Project of Jiangsu Province under Grant 2019010
Email Address of Submitting Authoryuanyuan@njupt.edu.cn
ORCID of Submitting Author0000-0003-1860-3275
Submitting Author's InstitutionNanjing University of Posts and Telecommunications
Submitting Author's Country