Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution
- DOI
- 10.1109/tgrs.2021.3120198
- Published
- 2022
- Container
- IEEE Transactions on Geoscience and Remote Sensing
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tgrs.2021.3120198,
title = {Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution},
author = {Swalpa Kumar Roy and Purbayan Kar and Danfeng Hong and Xin Wu and Antonio Plaza and Jocelyn Chanussot},
year = {2022},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
doi = {10.1109/tgrs.2021.3120198},
url = {https://doi.org/10.1109/tgrs.2021.3120198}
}RIS
TY - JOUR TI - Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution AU - Swalpa Kumar Roy AU - Purbayan Kar AU - Danfeng Hong AU - Xin Wu AU - Antonio Plaza AU - Jocelyn Chanussot PY - 2022 JO - IEEE Transactions on Geoscience and Remote Sensing DO - 10.1109/tgrs.2021.3120198 UR - https://doi.org/10.1109/tgrs.2021.3120198 ER -
APA
Roy, S. K., Kar, P., Hong, D., Wu, X., Plaza, A., & Chanussot, J. (2022). Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2021.3120198
Source records
- crossref · retrieved 2026-09-25T20:24:13.147Z