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

Abstract

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.