Jian Kang, Ruben Fernandez-Beltran, Xudong Kang, Jingen Ni, Antonio Plaza
This repo contains the main codes for the JSTARS paper: Noise-Tolerant Deep Neighborhood Embedding for Remotely Sensed Images with Label Noise We develop a new loss function called noise-tolerant deep neighborhood embedding (NTDNE) which can accurately encode the semantic relationships among RS scenes. Specifically, we target at maximizing the leave-one-out K-NN score for uncovering the inherent neighborhood structure among the images in feature space. Moreover, we down-weight the contribution of potential noisy images by learning their localized structure and pruning the images with low leave-oneout K-NN scores.
./train/main.py is the training script for NTDNE.
utils/metrics.pycontains the NTDNE loss implementation.
@article{kang2021NTNDE,
title={{Noise-Tolerant Deep Neighborhood Embedding for Remotely Sensed Images with Label Noise}},
author={Kang, Jian and Fernandez-Beltran, Ruben and Kang, Xudong and Ni, Jingen and Plaza, Antonio},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
year={2021},
note={DOI:10.1109/JSTARS.2021.3056661}
publisher={IEEE}
}