CC-SSL: A Self-Supervised Learning Framework for Crop Classification With Few Labeled Samples

Wang, Hengbin; Chang, Wanqiu; Yu, Yao; Liu, Diyou; Zhao, Yuanyuan; Li, Shaoming; Liu, Zhe; Zhang, Xiaodong

doi:10.1109/jstars.2022.3211994

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…In study area IV, Cropformer obtained AA of 81.52%, which was very close to the OA (84.39%) obtained in this region, while in study areas II and III it obtained AA of 64.81% and 61.23%, respectively, which was more than 20% different from the OA (86.32%, 85.70%) obtained, and this difference was more pronounced in RF (37%, 25%). This was due to the extremely unbalanced distribution of samples in study area II and III, where the sample size of maize was more than half of the total sample size, causing the AA to be insignificant, which is consistent with the findings of (Wang et al, 2022). Sample imbalance can bias the classification results toward the more numerous categories.…”

Section: Full-season Crop Classificationsupporting

confidence: 74%

Cropformer: A new generalized deep learning classification approach for multi-scenario crop classification

Wang

Chang²,

Yu³

et al. 2023

Front. Plant Sci.

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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.

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Section: Full-season Crop Classificationsupporting

confidence: 74%