1. bookVolume 72 (2021): Edizione 1 (February 2021)
Dettagli della rivista
License
Formato
Rivista
eISSN
1339-309X
Prima pubblicazione
07 Jun 2011
Frequenza di pubblicazione
6 volte all'anno
Lingue
Inglese
Accesso libero

Multiscale filter-based hyperspectral image classification with PCA and SVM

Pubblicato online: 18 Mar 2021
Volume & Edizione: Volume 72 (2021) - Edizione 1 (February 2021)
Pagine: 40 - 45
Ricevuto: 01 Nov 2020
Dettagli della rivista
License
Formato
Rivista
eISSN
1339-309X
Prima pubblicazione
07 Jun 2011
Frequenza di pubblicazione
6 volte all'anno
Lingue
Inglese

[1] Y. Guo, X. Yin, X. Zhao, D. Yang and Y. Bai, “Hyperspectral Image Classification with SVM and Guided Filter”, EURASIP Journal on Wireless Communications and Networking, Article number: 56, 2019.10.1186/s13638-019-1346-z Search in Google Scholar

[2] F. Zhou, R. Hang, Q. Liu and X. Yuan, “Hyperspectral Image Classification using Spectral-Spatial LSTMs”, Neurocomputing, vol. 328, pp. 39–47, 2019.10.1016/j.neucom.2018.02.105 Search in Google Scholar

[3] Y. Chen, N. M. Nasrabadi and T. D. Tran, “Hyperspectral Image Classification via Kernel Sparse Representation”, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 1, pp. 217–231, 2013.10.1109/TGRS.2012.2201730 Search in Google Scholar

[4] F. Melgani and L. Bruzzone, “Classification of Hyperspectral Remote Sensing Images with Support Vector Machines”, IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 8, pp. 1778–1790, 2004. Search in Google Scholar

[5] M. Fauvel, J. Chanussot and J. A. Benediktsson, “A Spatial-Spectral Kernel-Based Approach for the Classification of Remote-Sensing Images”, Pattern Recognition, vol. 45, no. 1, pp. 381–392, 2012.10.1016/j.patcog.2011.03.035 Search in Google Scholar

[6] G. Camps-Valls and L. Bruzzone, “Kernel-Based Methods for Hyper-Spectral Image Classification”, IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 6, pp. 1351–1362, 2005. Search in Google Scholar

[7] J. Li, P. R. Marpu, A. Plaza, J. M. Bioucas-Dias and J. A. Benediktsson, “Generalized Composite Kernel Framework for Hyper-Spectral Image Classification”, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 9, pp. 4816–4829, 2013. Search in Google Scholar

[8] Y. Chen, N. M. Nasrabadi and T. D. Tran, “Hyperspectral Image Classification using Dictionary-Based Sparse Representation”, IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 10, pp. 3973–3985, 2011. Search in Google Scholar

[9] J. Li, J. M. Bioucas-Dias and A. Plaza, “Spectral-Spatial Classification of Hyperspectral Data using Loopy Belief Propagation and Active Learning”, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 2, pp. 844–856, 2013.10.1109/TGRS.2012.2205263 Search in Google Scholar

[10] X. D. Kang, S. Li and J. A. Benediktsson, “Spectral-spatial HyperSpectral Image Classification with Edge-Preserving Filtering”, IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2666–2677, 2014. Search in Google Scholar

[11] G. Cheng, F. Zhu, S. Xiang, Y. Wang and X. Pan,“Semisupervised Hyperspectral Image Classification via Discriminant Analysis and Robust Regression”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 2, pp. 595–608, 2016.10.1109/JSTARS.2015.2471176 Search in Google Scholar

[12] Y. Chen, Z. Lin, X. Zhao, G. Wang and Y. Gu, “Deep Learning-Based Classification of Hyperspectral Data”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 2094–2107, 2014. Search in Google Scholar

[13] H. Liu, K. Xia, T. Li, J. Ma and E. Owoola, “Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial-Spectral Weight Manifold Embedding”, Sensors 2020, 20, 4413; doi10.3390/s20164413.10.3390/s20164413747247732784692 Search in Google Scholar

[14] I. T. Jolliffe, Principal Component Analysis, second edition (Springer), 2002. Search in Google Scholar

[15] C. Cortes and V. N. Vapnik, “Support-Vector Networks”, Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.10.1007/BF00994018 Search in Google Scholar

[16] S. Damelin and W. Miller, The mathematics of signal processing, Cambridge University Press, ISBN 978-1107601048, 2011.10.1017/CBO9781139003896 Search in Google Scholar

[17] C. C. Chang and C. J. Lin. “LIBSVM: a Library for Support Vector Machines”, ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 27, pp. 1–27, 2011.10.1145/1961189.1961199 Search in Google Scholar

[18] S. Roweis and L. Saul, “Nonlinear Dimensionality Reduction by Locally Linear Embedding”, Science, vol. 290, no. 5500, pp. 2323–2326, 2000. Search in Google Scholar

Articoli consigliati da Trend MD

Pianifica la tua conferenza remota con Sciendo