TY - JOUR
T1 - Polar-Spatial Feature Fusion Learning with Variational Generative-Discriminative Network for PolSAR Classification
AU - Wen, Zaidao
AU - Wu, Qian
AU - Liu, Zhunga
AU - Pan, Quan
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Feature learning-based polarimetric synthetic aperture radar (PolSAR) classification model will generally suffer from the challenge of deficient labeled pixels. In this paper, we propose a novel generative-discriminative network for PolSAR polar-spatial feature fusion learning and classification, which comprises of a deep generative network and a discriminative network with their bottom layers shared. With this architecture, it enables to make use of both labeled and unlabeled pixels in a PolSAR image for model learning in a semisupervised way. Moreover, the proposed network imposes a Gaussian random field prior and a conditional random field posterior on the learned fusion features and the output label configuration, respectively. Without the need of the complicated recurrent iterations, our network can still efficiently produce the structured fusion feature as well as a smoothed classification map by involving some auxiliary variables, and it is specifically optimized via variational inference within an alternating direction method of multipliers iteration scheme. Extensive experiments on different benchmark PolSAR imageries demonstrate the effectiveness and superiority of the proposed network. Compared with other state-of-the-art algorithms of PolSAR feature learning and classification, our model can achieve a much better performance in terms of the visual quality of the label map and overall classification accuracy, facilitating the much less labeling pixels.
AB - Feature learning-based polarimetric synthetic aperture radar (PolSAR) classification model will generally suffer from the challenge of deficient labeled pixels. In this paper, we propose a novel generative-discriminative network for PolSAR polar-spatial feature fusion learning and classification, which comprises of a deep generative network and a discriminative network with their bottom layers shared. With this architecture, it enables to make use of both labeled and unlabeled pixels in a PolSAR image for model learning in a semisupervised way. Moreover, the proposed network imposes a Gaussian random field prior and a conditional random field posterior on the learned fusion features and the output label configuration, respectively. Without the need of the complicated recurrent iterations, our network can still efficiently produce the structured fusion feature as well as a smoothed classification map by involving some auxiliary variables, and it is specifically optimized via variational inference within an alternating direction method of multipliers iteration scheme. Extensive experiments on different benchmark PolSAR imageries demonstrate the effectiveness and superiority of the proposed network. Compared with other state-of-the-art algorithms of PolSAR feature learning and classification, our model can achieve a much better performance in terms of the visual quality of the label map and overall classification accuracy, facilitating the much less labeling pixels.
KW - Deep learning
KW - discriminative model
KW - feature fusion
KW - generative model
KW - polarimetric synthetic aperture radar (PolSAR) classification
KW - variational inference
UR - http://www.scopus.com/inward/record.url?scp=85074459735&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2019.2923738
DO - 10.1109/TGRS.2019.2923738
M3 - 文章
AN - SCOPUS:85074459735
SN - 0196-2892
VL - 57
SP - 8914
EP - 8927
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 11
M1 - 8765386
ER -