TY - GEN
T1 - A SEMI-SUPERVISED SIAMESE NETWORK WITH LABEL FUSION FOR REMOTE SENSING IMAGE SCENE CLASSIFICATION
AU - Miao, Wang
AU - Geng, Jie
AU - Deng, Xinyang
AU - Jiang, Wen
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Remote sensing image scene classification, which requires large amounts of labeled data, plays a critical role in a range of fields.However, in the actual complex environment, the obtained remote sensing images are sometimes unlabeled due to data perturbation and the cost of manual labeling, which limits the training effect and generalization ability. To solve this issue, a semi-supervised siamese network with label fusion is proposed for remote sensing image scene classification.The siamese network is developed to extract features from remote sensing image, where loss function based on the low-entropy principle is constructed to select the unlabeled data as pseudo-label samples. The labeled and pseudo-label samples are mixed to further train the siamese network. The results on UC Merced dataset and WHU-RS19 show that our method is capable to achieve excellent performance compared with other semi-supervised learning methods.
AB - Remote sensing image scene classification, which requires large amounts of labeled data, plays a critical role in a range of fields.However, in the actual complex environment, the obtained remote sensing images are sometimes unlabeled due to data perturbation and the cost of manual labeling, which limits the training effect and generalization ability. To solve this issue, a semi-supervised siamese network with label fusion is proposed for remote sensing image scene classification.The siamese network is developed to extract features from remote sensing image, where loss function based on the low-entropy principle is constructed to select the unlabeled data as pseudo-label samples. The labeled and pseudo-label samples are mixed to further train the siamese network. The results on UC Merced dataset and WHU-RS19 show that our method is capable to achieve excellent performance compared with other semi-supervised learning methods.
KW - Classification
KW - Deep neural networks
KW - Remote sensing image
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85126032670&partnerID=8YFLogxK
U2 - 10.1109/IGARSS47720.2021.9553501
DO - 10.1109/IGARSS47720.2021.9553501
M3 - 会议稿件
AN - SCOPUS:85126032670
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4932
EP - 4935
BT - IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Y2 - 12 July 2021 through 16 July 2021
ER -