Estimating Optimal Number of Compressively Sensed Bands for Hyperspectral Classification via Feature Selection
- DOI
- 10.1109/jstars.2021.3128288
- Published
- 2021
- Container
- IEEE Journal of Selected Topics in Applied Earth Observations 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/jstars.2021.3128288,
title = {Estimating Optimal Number of Compressively Sensed Bands for Hyperspectral Classification via Feature Selection},
author = {C. J. Della Porta and Chein-I Chang},
year = {2021},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
doi = {10.1109/jstars.2021.3128288},
url = {https://doi.org/10.1109/jstars.2021.3128288}
}RIS
TY - JOUR TI - Estimating Optimal Number of Compressively Sensed Bands for Hyperspectral Classification via Feature Selection AU - C. J. Della Porta AU - Chein-I Chang PY - 2021 JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing DO - 10.1109/jstars.2021.3128288 UR - https://doi.org/10.1109/jstars.2021.3128288 ER -
APA
Porta, C. J. D., & Chang, C. (2021). Estimating Optimal Number of Compressively Sensed Bands for Hyperspectral Classification via Feature Selection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/jstars.2021.3128288
Source records
- crossref · retrieved 2026-09-26T01:31:19.815Z