TY - JOUR
T1 - The 2022 IEEE GRSS data fusion contest
T2 - Semisupervised learning [technical committees]
AU - Hänsch, Ronny
AU - Persello, C.
AU - Vivone, Gemine
AU - Castillo Navarro, Javiera
AU - Boulch, Alexandre
AU - Lefevre, Sebastien
AU - Le Saux, Bertrand
N1 - Publisher Copyright:
© IEEE.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - Data availability plays a central role in any machine learning setup, especially since the rise of deep learning. Although input data are often available in abundance, reference data used to train and evaluate corresponding approaches are usually scarce due to the high cost of obtaining them. Although this is not limited to remote sensing, it is of particular importance in Earth-observation applications. Semisupervised learning is one approach to mitigate this challenge and leverage the large amount of available input data while relying only on a small, annotated training set.
AB - Data availability plays a central role in any machine learning setup, especially since the rise of deep learning. Although input data are often available in abundance, reference data used to train and evaluate corresponding approaches are usually scarce due to the high cost of obtaining them. Although this is not limited to remote sensing, it is of particular importance in Earth-observation applications. Semisupervised learning is one approach to mitigate this challenge and leverage the large amount of available input data while relying only on a small, annotated training set.
KW - 22/2 OA procedure
KW - ITC-ISI-JOURNAL-ARTICLE
U2 - 10.1109/MGRS.2022.3144291
DO - 10.1109/MGRS.2022.3144291
M3 - Article
AN - SCOPUS:85129480583
SN - 2473-2397
VL - 10
SP - 334
EP - 337
JO - IEEE geoscience and remote sensing magazine
JF - IEEE geoscience and remote sensing magazine
IS - 1
ER -