Evaluation of dimensionality reduction techniques on hybrid CNN–based HSI classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s12517-021-09143-3.pdf
Reference26 articles.
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2. S Swain et al. (2021) Dimensionality reduction and classification in hyperspectral images using deep learning. In: Bandyopadhyay M., Rout M., Chandra Satapathy S. (eds) Machine learning approaches for urban computing. Studies in Computational Intelligence. vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-16-0935-0_6
3. A Banerjee et al. (2021) Extraction of information from hyperspectral imaging using deep learning. In: Bandyopadhyay M., Rout M., Chandra Satapathy S. (eds) Machine learning approaches for urban computing. Studies in Computational Intelligence. vol 968. Springer, Singapore. https://doi.org/10.1007/978- 981–16–0935–0_3
4. Roy SK et al (2020) HybridSN: exploring 3-D–2-D CNN feature hierarchy for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters (IEEE GEOSCI REMOTE s) 17(2):277–281. https://doi.org/10.1109/LGRS.2019.2918719
5. Bruce LM, Koger CH, Li J (2002) Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction. IEEE GEOSCI REMOTE S 40(10):2331–2338. https://doi.org/10.1109/TGRS.2002.804721
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