Pooled hybrid-spectral for hyperspectral image classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-022-13721-2.pdf
Reference24 articles.
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2. Ben Hamida A, Benoit A, Lambert P, Ben Amar C (2018) 3-D deep learning approach for remote sensing image classification. IEEE Trans Geosci Remote Sens 56(8):4420–4434. https://doi.org/10.1109/TGRS.2018.2818945
3. Bioucas-Dias JM, Plaza A, Camps-Valls G, Scheunders P, Nasrabadi N, Chanussot J (2013) Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci Remote Sens Mag 1(2):6–36
4. Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251. https://doi.org/10.1109/TGRS.2016.2584107
5. Fang L, Wang C, Li S, Benediktsson JA (2017) Hyperspectral image classification via Multiple-Feature-Based adaptive sparse representation. IEEE Trans Instrum Meas 66(7):1646–1657. https://doi.org/10.1109/TIM.2017.2664480
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