Multiple-feature-driven co-training method for crop mapping based on remote sensing time series imagery

ABSTRACT

Remote sensing time series imagery (RSTSI) provides a useful tool for crop mapping, as it provides crucial spectral, temporal, and spatial (STS) features. However, its high dimensionality coupled with the limited number of training samples leads to an ill-posed classification problem and the Hughes phenomenon. To solve this problem, this study presents a multiple-feature-driven co-training method (MFDC) for accurately mapping crop types based on RSTSI with a limited number of training samples. In MFDC, four complementary pre-defined views, which represent STS features, are generated for the utilization of multiple features. Then, to enhance the classifier’s generalization ability, a novel labelled sample augmentation method that combines the Breaking Tiles algorithm and co-training is proposed. Third, to ensure the effectiveness of ensemble learning in co-training as well as to further speed up the learning process, a multi-view semi-supervised feature learning algorithm that expands the single view semi-supervised learning algorithm to multiple views is proposed and embedded in co-training. Finally, a weighted majority vote method is utilized to obtain the classification results. The experimental results for study areas in the United States indicate that the proposed method can accurately map crop types with a limited number of labelled training samples without a significant computational cost.

Acknowledgements

We would like to thank the anonymous reviewers for their constructive comments and the high-performance computing support from the Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University (Available online: https://gda.bnu.edu.cn/).

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This research was funded by the second Tibetan Plateau Scientific Expedition and Research Program (STEP) under Grant [2019QZKK0608] and National Natural Science Foundation of China under Grant [41901316].