Cargando…
RS-SSKD: Self-Supervision Equipped with Knowledge Distillation for Few-Shot Remote Sensing Scene Classification
While growing instruments generate more and more airborne or satellite images, the bottleneck in remote sensing (RS) scene classification has shifted from data limits toward a lack of ground truth samples. There are still many challenges when we are facing unknown environments, especially those with...
Autores principales: | Zhang, Pei, Li, Ying, Wang, Dong, Wang, Jiyue |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956409/ https://www.ncbi.nlm.nih.gov/pubmed/33668138 http://dx.doi.org/10.3390/s21051566 |
Ejemplares similares
-
Self-supervised learning for remote sensing scene classification under the few shot scenario
por: Alosaimi, Najd, et al.
Publicado: (2023) -
Insights into few shot learning approaches for image scene classification
por: Soudy, Mohamed, et al.
Publicado: (2021) -
Semi-supervised bidirectional alignment for Remote Sensing cross-domain scene classification
por: Huang, Wei, et al.
Publicado: (2023) -
Unsupervised Few-Shot Feature Learning via Self-Supervised Training
por: Ji, Zilong, et al.
Publicado: (2020) -
Semi-supervised few-shot learning approach for plant diseases recognition
por: Li, Yang, et al.
Publicado: (2021)