codes for RS paper: High-Rankness Regularized Semi-supervised Deep Metric Learning for Remote Sensing Imagery
Jian Kang, Ruben Fernandez-Beltran, Zhen Ye, Xiaohua Tong, Pedram Ghamisi, Antonio Plaza.
This repo contains the codes for the MDPI RS paper: High-Rankness Regularized Semi-supervised Deep Metric Learning for Remote Sensing Imagery. we reformulate the deep metric learning scheme in a semi-supervised manner to effectively characterize RS scenes. Specifically, we aim at learning metric spaces by utilizing the supervised information from a small number of labeled RS images and exploring the potential decision boundaries for massive sets of unlabeled aerial scenes. In order to reach this goal, a joint loss function, composed of a normalized softmax loss with margin and a high-rankness regularization term, is proposed, as well as its corresponding optimization algorithm. Some codes are modified from ArcFace and BNM.
./train_MNCE_BNM/main.py is the script of the proposed method for training and validation.
@article{kang2020highrank,title={{High-Rankness Regularized Semi-supervised Deep Metric Learning for Remote Sensing Imagery}},author={Kang, Jian and Fernandez-Beltran, Ruben and Ye, Zhen and Tong, Xiaohua and Ghamisi, Pedram and Plaza, Antonio},journal={Remote Sensing},year={2020},note={DOI:}publisher={MDPI}}
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[2] Cui, Shuhao, et al. “Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.