Hengbin Wang Wanqiu Chang Yu Yao Zhiying Yao Yuanyuan Zhao Shaoming Li Zhe Liu Xiaodong Zhang
Abstract
Accurate and efficient crop classification using remotely sensed data can provide fundamental and important information for crop yield estimation. Existing crop classification approaches are usually designed to be strong in some specific scenarios but not for multi-scenario crop classification. In this study, we proposed a new deep learning approach for multi-scenario crop classification, named Cropformer. Cropformer can extract global features and local features, to solve the problem that current crop classification methods extract a single feature. Specifically, Cropformer is a two-step classification approach, where the first step is self-supervised pre-training to accumulate knowledge of crop growth, and the second step is a fine-tuned supervised classification based on the weights from the first step. The unlabeled time series and the labeled time series are used as input for the first and second steps respectively. Multi-scenario crop classification experiments including full-season crop classification, in-season crop classification, few-sample crop classification, and transfer of classification models were conducted in five study areas with complex crop types and compared with several existing competitive approaches. Experimental results showed that Cropformer can not only obtain a very significant accuracy advantage in crop classification, but also can obtain higher accuracy with fewer samples. Compared to other approaches, the classification performance of Cropformer during model transfer and the efficiency of the classification were outstanding. The results showed that Cropformer could build up a priori knowledge using unlabeled data and learn generalized features using labeled data, making it applicable to crop classification in multiple scenarios.
fpls-14-1130659.pdf