TY - GEN
T1 - Unsupervised Temporal-Adaptation with Multiple Geodesic Flow Kernels for Hyperspectral Image Classification
AU - Liu, Tianzhu
AU - Gu, Yanfeng
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The miniaturization of hyperspectral sensors and the popularity of the unmanned aerial vehicle (UAV) make it possible to obtain a series of hyperspectral images (HSIs) in the same geographical area at different time-points by same or different sensors. When classifying these multi-temporal HSIs, temporal-adaptation is required to deal with the spectral drift and band inconsistency problems. since most studies focus on semi-supervised domain adaptation (DA) strategy, and spatial features are usually absent during most of the DA procedure, an unsupervised temporal-adaptation method is realized by spatial-spectral multiple Geodesic Flow Kernels (S2-GFKs) to classify bi-temporal HSIs. Experiments conducted on two real HSI datasets and compared with several well-known methods demonstrate the availability of the proposed model.
AB - The miniaturization of hyperspectral sensors and the popularity of the unmanned aerial vehicle (UAV) make it possible to obtain a series of hyperspectral images (HSIs) in the same geographical area at different time-points by same or different sensors. When classifying these multi-temporal HSIs, temporal-adaptation is required to deal with the spectral drift and band inconsistency problems. since most studies focus on semi-supervised domain adaptation (DA) strategy, and spatial features are usually absent during most of the DA procedure, an unsupervised temporal-adaptation method is realized by spatial-spectral multiple Geodesic Flow Kernels (S2-GFKs) to classify bi-temporal HSIs. Experiments conducted on two real HSI datasets and compared with several well-known methods demonstrate the availability of the proposed model.
KW - GFK
KW - HSI
KW - Unsupervised temporal-adaptation
KW - multiple kernel learning
KW - spatial-spectral classification
UR - https://www.scopus.com/pages/publications/85077678228
U2 - 10.1109/IGARSS.2019.8898677
DO - 10.1109/IGARSS.2019.8898677
M3 - 会议稿件
AN - SCOPUS:85077678228
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 10111
EP - 10114
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -