Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging
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- Authors
- Zabalza, J., Ren, J., Zheng, J., Zhao, H., Qing, C., Yang, Z., Du, P., & Marshall, S.
- Affiliations
- University of Strathclyde (GB)
- Northwestern Polytechnical University (CN)
- South China University of Technology (CN)
- Guangdong University of Technology (CN)
- Nanjing University (CN)
- Corresponding author
- Ren, Jinchang
- ORCID
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 185
- Pages
- 1-10
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2219715551
- Cited by
- 389
- References
- 30
- Citations by year
- 2026: 5 · 2025: 20 · 2024: 34 · 2023: 50 · 2022: 36 · 2021: 62 · 2020: 70 · 2019: 34 · 2018: 47 · 2017: 24 · 2016: 7
- FWCI
- 41.69
- Type
- journal article
- Language
- en
- Update notices
- erratum (2016-11-19)
- Open access
- Locations
- Neurocomputing (publishedVersion)
- Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61272381, 61471132, 61401163)
- Science and Technology Major Project of Education Department of Guangdong Province (2014KZDXM060)
- Fundamental Research Funds for the Central Universities (2015ZZ032)
- Science and Technology Project of Guangzhou City (2014J4100078)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Keywords
- stacked autoencoder, dimensionality reduction, feature extraction, hyperspectral imaging, hyperspectral remote sensing, data segmentation, classification
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