Chinese Journal of Lasers, Volume. 47, Issue 7, 710001(2020)
Hyperspectral Remote Sensing Image Classification Based on Local Reconstruction Fisher Analysis
[2] Wang X F, Zhang J P, Yan Q J et al. Target detection for hyperspectral image based on support vector data description[J]. Chinese Journal of Lasers, 41, s114003(2014).
[4] Huang H, Shi G Y, He H B et al. Dimensionality reduction of hyperspectral imagery based on spatial-spectral manifold learning[J]. IEEE Transactions on Cybernetics, 50, 2604-2616(2020).
[5] Li X Y, Zhang L F, You J. Locally weighted discriminant analysis for hyperspectral image classification[J]. Remote Sensing, 11, 109(2019).
[6] Huang H, Duan Y L, He H B et al. Spatial-spectral local discriminant projection for dimensionality reduction of hyperspectral image[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 77-93(2019).
[7] Luo F L, Du B, Zhang L P et al. Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral image[J]. IEEE Transactions on Cybernetics, 49, 2406-2419(2019).
[8] Luo F L, Zhang L P, Du B et al. Discriminant spatial-spectral hypergraph learning for hyperspectral image classification. [C]∥IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, July 22-27, 2018. Valencia. New York: IEEE, 8480-8483(2018).
[9] Xu Y H, Zhang L P, Du B et al. Spectral-spatial unified networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 5893-5909(2018).
[10] Arsa D M S, Sanabila H R, Rachmadi M F et al. Improving principal component analysis performance for reducing spectral dimension in hyperspectral image classification. [C]∥2018 International Workshop on Big Data and Information Security (IWBIS), May 12-13, 2018. Jakarta. New York: IEEE, 123-128(2018).
[11] Wen J, Fang X Z, Cui J R et al. Robust sparse linear discriminant analysis[J]. IEEE Transactions on Circuits and Systems for Video Technology, 29, 390-403(2019).
[12] Li H, Jiang T, Zhang K. Efficient and robust feature extraction by maximum margin criterion[J]. IEEE Transactions on Neural Networks, 17, 157-165(2006).
[13] Shao Z F, Zhang L. Sparse dimensionality reduction of hyperspectral image based on semi-supervised local Fisher discriminant analysis[J]. International Journal of Applied Earth Observation and Geoinformation, 31, 122-129(2014).
[14] Fan M Y, Qiao H, Zhang B et al. Isometric multi-manifold learning for feature extraction. [C]∥2012 IEEE 12th International Conference on Data Mining, December 10-13, 2012. Brussels, Belgium. New York: IEEE, 241-250(2012).
[15] Roweis S T. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 290, 2323-2326(2000).
[16] He X F, Cai D, Yan S C et al. Neighborhood preserving embedding. [C]∥Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, October 17-21, 2005. Beijing, China. New York: IEEE, 1208-1213(2005).
[17] Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 15, 1373-1396(2003).
[18] He X F, Niyogi P. Locality preserving projections. [C]∥17th Annual Conference on Neural Information Processing Systems (NIPS), Dec. 08, 2003, Canada. Massachusetts: NIPS, 153-160(2004).
[19] Yan S C, Xu D, Zhang B et al. Graph embedding and extensions: a general framework for dimensionality reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 40-51(2007).
[20] Luo F L, Huang H, Duan Y L et al. Local geometric structure feature for dimensionality reduction of hyperspectral imagery[J]. Remote Sensing, 9, 790(2017).
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Liu Jiamin, Yang Song, Huang Hong. Hyperspectral Remote Sensing Image Classification Based on Local Reconstruction Fisher Analysis[J]. Chinese Journal of Lasers, 2020, 47(7): 710001
Category: remote sensing and sensor
Received: Dec. 23, 2019
Accepted: --
Published Online: Jul. 10, 2020
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