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

Labeled samples with real crop types are important for crop classification, but the acquisition of large batches of labeled samples will consume many resources, so it is necessary to study crop classification based on few labeled samples or no labeled samples. To solve the problem of labeling sample...

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Váldodahkkit:	Hengbin Wang, Wanqiu Chang, Yu Yao, Diyou Liu, Yuanyuan Zhao, Shaoming Li, Zhe Liu, Xiaodong Zhang
Materiálatiipa:	Artigo
Giella:	Inglês
Almmustuhtton:	IEEE 2022-01-01
Ráidu:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Fáttát:	Crop classification data augmentation few labeled samples sample balance self-supervised learning (SSL)
Liŋkkat:	https://ieeexplore.ieee.org/document/9910397/
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