Improving the crop classification performance by unlabeled remote sensing data

Accurate and timely crop classification results play a crucial role in providing data support for agricultural policy-making and crop yield estimation. However, the current development of crop classification faces a bottleneck in improving classification performance due to limited labeled samples and saturated classification algorithms. In this study, we propose a novel method to improve crop classification performance by leveraging unlabeled remote sensing data (URSD). Importantly, our method does not necessitate a large number of labeled samples or significant modifications to the classification algorithm. Instead, it relies on a unique self-supervised training approach and a substantial amount of URSD. Specifically, we develop a self-supervised classification framework based on a Multilayer Perceptron (MLP) and introduce a self-supervised training approach that takes into account both temporal and spectral factors. Additionally, we construct a historical sample classification model based on crop growth knowledge, emphasizing the correlation of local time series. We evaluate the proposed method using four study areas in China. The analysis of pre-training data types reveals that our method not only improves the classification performance of current year samples but also demonstrates noticeable improvement in classifying historical samples. The classification method analysis demonstrates the ability of our proposed self-supervised learning training approach to accumulate more prior knowledge. Overall, these results highlight the advantages of our method in terms of classification efficiency and performance improvement.