TY - JOUR
T1 - Foreground-Background Contrastive Learning for Few-Shot Remote Sensing Image Scene Classification
AU - Geng, Jie
AU - Xue, Bohan
AU - Jiang, Wen
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2023
Y1 - 2023
N2 - Few-shot learning (FSL) aims to train a model with limited samples for identifying novel category samples. As for remote sensing (RS) images, complex backgrounds may lead to large intraclass differences, and the number of labeled samples is quite smaller than that of large datasets, which both influence the classification performance. To solve these issues, a foreground-background contrastive learning (FBCL) is proposed for few-shot RS image scene classification. Specifically, a foreground-background separation (FBS) module is proposed to separate features between objects and background with supervised contrastive learning (SCL), which aims to improve the ability to distinguish foreground and background regions of RS images. Moreover, a channel weight allocator is proposed to balance features of different dimensions, which can take full advantage of RS image information. Experiments on three RS datasets prove that the proposed few-shot method is able to produce superior classification results than other related approaches.
AB - Few-shot learning (FSL) aims to train a model with limited samples for identifying novel category samples. As for remote sensing (RS) images, complex backgrounds may lead to large intraclass differences, and the number of labeled samples is quite smaller than that of large datasets, which both influence the classification performance. To solve these issues, a foreground-background contrastive learning (FBCL) is proposed for few-shot RS image scene classification. Specifically, a foreground-background separation (FBS) module is proposed to separate features between objects and background with supervised contrastive learning (SCL), which aims to improve the ability to distinguish foreground and background regions of RS images. Moreover, a channel weight allocator is proposed to balance features of different dimensions, which can take full advantage of RS image information. Experiments on three RS datasets prove that the proposed few-shot method is able to produce superior classification results than other related approaches.
KW - Contrastive learning
KW - few-shot learning (FSL)
KW - remote sensing (RS) image
KW - scene classification
UR - http://www.scopus.com/inward/record.url?scp=85163702886&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2023.3290794
DO - 10.1109/TGRS.2023.3290794
M3 - 文章
AN - SCOPUS:85163702886
SN - 0196-2892
VL - 61
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
M1 - 5614112
ER -