TY - GEN
T1 - Noise-Tolerant Unsupervised Classification for PolSAR Images Via Deep Clustering And Markov Random Field
AU - Yang, Sihan
AU - Bi, Haixia
AU - Wang, Xiaotian
AU - Hong, Danfeng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Due to the difficulty of obtaining manual annotations for polarimetric synthetic aperture radar (PolSAR) images, the problem of analyzing these images without or with few labels has become a current challenge. Considering the scarcity of labels, this paper proposes a noise-tolerant deep clustering-based PolSAR image classification that mainly uses autoencoders to learn discriminative features. In addition, in order to improve the performance and noise resistance of this method, we adopt Markov Random Field (MRF) to enhance the smoothness of class labels. We conducted experiments on a real benchmark PolSAR image, and the results show that our method achieves state-of-the-art PolSAR image classification results without any manual annotations.
AB - Due to the difficulty of obtaining manual annotations for polarimetric synthetic aperture radar (PolSAR) images, the problem of analyzing these images without or with few labels has become a current challenge. Considering the scarcity of labels, this paper proposes a noise-tolerant deep clustering-based PolSAR image classification that mainly uses autoencoders to learn discriminative features. In addition, in order to improve the performance and noise resistance of this method, we adopt Markov Random Field (MRF) to enhance the smoothness of class labels. We conducted experiments on a real benchmark PolSAR image, and the results show that our method achieves state-of-the-art PolSAR image classification results without any manual annotations.
KW - Markov random field (MRF)
KW - Unsupervised feature learning
KW - autoencoder (AE)
KW - polarimetric synthetic aperture radar (PolSAR) image classification
UR - https://www.scopus.com/pages/publications/85178344796
U2 - 10.1109/IGARSS52108.2023.10281880
DO - 10.1109/IGARSS52108.2023.10281880
M3 - 会议稿件
AN - SCOPUS:85178344796
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 5970
EP - 5973
BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Y2 - 16 July 2023 through 21 July 2023
ER -