The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Towards Energy-Efficient Hyperspectral Image Processing Inside Camera Pixels
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“Because of the absence of wireless connectivity in remote regions, they proposed to use an offline model that achieves 96% accuracy while conserving battery power.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [85]
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
Deep learning-based classification of hyperspectral data
Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1602.02830 · Cited by 2,106
Zhong, Z., Li, J., Luo, Z., & Chapman, M. A. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2755542 · Cited by 2,033
HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification
Roy, S. K., Krishna, G., Dubey, S. R., & Chaudhuri, B. B. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2918719 · Cited by 2,010
Adão, T., Hruška, J., Pádua, L., et al. (2017) · Remote Sensing · doi:10.3390/rs9111110 · Cited by 1,319
Ultrafast machine vision with 2D material neural network image sensors
Mennel, L., Symonowicz, J., Wachter, S., Polyushkin, D. K., Molina‐Mendoza, A. J., & Mueller, T. (2020) · Nature · doi:10.1038/s41586-020-2038-x · Cited by 1,180
3-D Deep Learning Approach for Remote Sensing Image Classification
Hamida, A. B., Benoît, A., Lambert, P., & Amar, C. B. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2818945 · Cited by 950
Review of snapshot spectral imaging technologies
Hagen, N. A., & Kudenov, M. W. (2013) · Optical Engineering · doi:10.1117/1.oe.52.9.090901 · Cited by 746
Research works printed but not linked: 2
- Sony to release world’s first intelligent vision sensors with AI processing functionality (2020). https://www.sony.com/en/SonyInfo/News/Press/202005/20-037E/. Accessed 12 Jan 2022
- Alipour-Fard, T., Paoletti, M.E., Haut, J.M., Arefi, H., Plaza, J., Plaza, A.: Multibranch selective kernel networks for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 1(1), 1–5 (2020)
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A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366
Metadata
- Authors
- Datta, G., Yin, Z., Jacob, A. P., Jaiswal, A. R., & Beerel, P. A.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Datta, Gourav
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 303-316
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4321353513
- Cited by
- 5
- References
- 36
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 3
- FWCI
- 2.35
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Funders
- Defense Advanced Research Projects Agency (HR00112190120)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › CCD and CMOS Imaging Sensors
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Keywords
- hyperspectral image analysis, energy efficiency, convolutional neural networks, data compression, bandwidth reduction, hyperspectral image classification
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