IEEE Geoscience and Remote Sensing Magazine - December 2020 - 86

Intell., vol. 29, no. 1, pp. 40-51, Jan 2007. doi: 10.1109/TPAMI.
2007.250598.
[50]	 X. Wu, D. Hong, J. Tian, J. Chanussot, W. Li, and R. Tao, " Orsim
detector: A novel object detection framework in optical remote
sensing imagery using spatial-frequency channel features, "
IEEE Trans. Geosci. Remote Sens., vol. 57, no. 7, pp. 5146-5158,
2019. doi: 10.1109/TGRS.2019.2897139.
[51]	 R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, vol. 3. New York: Wiley, 1973.
[52]	 R. Hang, Q. Liu, H. Song, and Y. Sun, " Matrix-based discriminant subspace ensemble for hyperspectral image spatial-spectral feature fusion, " IEEE Trans. Geos. Remote Sens., vol. 54, no. 2,
pp. 783-794, 2016. doi: 10.1109/TGRS.2015.2465899.
[53]	 C. Lee and D. Landgrebe, " Feature extraction based on decision
boundaries, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, no.
4, pp. 388-400, 1993. doi: 10.1109/34.206958.
[54]	 B. Guo, S. R. Gunn, R. I. Damper, and J. D. Nelson, " Band selection for hyperspectral image classification using mutual
information, " IEEE Geosci. Remote Sens. Lett., vol. 3, no. 4, pp.
522-526, 2006. doi: 10.1109/LGRS.2006.878240.
[55]	 H. Shi, Y. Shen, and Z. Liu, " Hyperspectral bands reduction
based on rough sets and fuzzy c-means clustering, " in Proc.
IEEE Instrumentation Technology Conf. (IMTC), 2003, vol. 2, pp.
1053-1056. doi: 10.1109/IMTC.2003.1207913.
[56]	 J. Yang and J. Yang, " Why can LDA be performed in PCA transformed space? " Pattern Recognit., vol. 36, no. 2, pp. 563-566,
2003. doi:10.1016/S0031-3203(02)00048-1.
[57]	 T. V. Bandos, L. Bruzzone, and G. Camps-Valls, " Classification
of hyperspectral images with regularized linear discriminant
analysis, " IEEE Trans. Geos. Remote Sens., vol. 47, no. 3, pp. 862-
873, 2009. doi: 10.1109/TGRS.2008.2005729.
[58]	 M. Sugiyama, " Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis, " J. Mach. Learn.
Res., vol. 8, pp. 1027-1061, May 2007.
[59]	 M. Imani and H. Ghassemian, " Feature space discriminant
analysis for hyperspectral data feature reduction, " ISPRS J.
Photogramm. Remote. Sens., vol. 102, pp. 1-13, Apr. 2015. doi:
10.1016/j.isprsjprs.2014.12.024.
[60]	 S. Patra, P. Modi, and L. Bruzzone, " Hyperspectral band selection
based on rough set, " IEEE Trans. Geosci. Remote Sens., vol. 53, no. 10,
pp. 5495-5503, 2015. doi: 10.1109/TGRS.2015.2424236.
[61]	 X. Cao, T. Xiong, and L. Jiao, " Supervised band selection using local spatial information for hyperspectral image, " IEEE
Geosci. Remote Sens. Lett., vol. 13, no. 3, pp. 329-333, 2016. doi:
10.1109/LGRS.2015.2511186.
[62]	 M. Belkin and P. Niyogi, " Laplacian eigenmaps for dimensionality
reduction and data representation, " Neural Comput., vol. 15, no. 6,
pp. 1373-1396, 2003. doi: 10.1162/089976603321780317.
[63]	 X. He and P. Niyogi, " Locality preserving projections, " in Proc.
Neural Information Processing Systems, 2004, pp. 153-160.
[64]	 L. Zelnik-Manor and P. Perona, " Self-tuning spectral clustering, "
in Proc. Neural Information Processing Systems, 2005, pp. 1601-1608.
[65]	 N. H. Ly, Q. Du, and J. E. Fowler, " Sparse graph-based discriminant analysis for hyperspectral imagery, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 7, pp. 3872-3884, 2014. doi: 10.1109/
TGRS.2013.2277251.

86

[66]	 N. H. Ly, Q. Du, and J. E. Fowler, " Collaborative graph-based
discriminant analysis for hyperspectral imagery, " IEEE J. Sel.
Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2688-
2696, 2014. doi: 10.1109/JSTARS.2014.2315786.
[67]	 D. Hong, N. Yokoya, and X. X. Zhu, " Learning a robust local
manifold representation for hyperspectral dimensionality reduction, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 10, no.
6, pp. 2960-2975, 2017. doi: 10.1109/JSTARS.2017.2682189.
[68]	 D. Hong, N. Yokoya, J. Chanussot, J. Xu, and X. X. Zhu, " Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction, " ISPRS J. Photogramm. Remote. Sens., vol. 158,
pp. 35-49, Dec. 2019. doi: 10.1016/j.isprsjprs.2019.09.008.
[69]	 L. Zhang, M. Yang, and X. Feng, " Sparse representation or collaborative representation: Which helps face recognition? " in
Proc. IEEE Int. Conf. Computer Vision, 2011, pp. 471-478. doi:
10.1109/ICCV.2011.6126277.
[70]	 D. Hong and X. X. Zhu, " SULoRA: Subspace unmixing with
low-rank attribute embedding for hyperspectral data analysis, "
IEEE J. Sel. Topics Signal Process., vol. 12, no. 6, pp. 1351-1363,
2018. doi: 10.1109/JSTSP.2018.2877497.
[71]	 D. Hong, N. Yokoya, J. Chanussot, and X. X. Zhu, " An augmented linear mixing model to address spectral variability for
hyperspectral unmixing, " IEEE Trans. Image Process., vol. 28, no.
4, pp. 1923-1938, 2019. doi: 10.1109/TIP.2018.2878958.
[72]	 H. Huang, F. Luo, J. Liu, and Y. Yang, " Dimensionality reduction
of hyperspectral images based on sparse discriminant manifold embedding, " ISPRS J. Photogramm. Remote Sens., vol. 106,
pp. 42-54, Aug. 2015. doi: 10.1016/j.isprsjprs.2015.04.015.
[73]	 Z. Xue, P. Du, J. Li, and H. Su, " Simultaneous sparse graph embedding for hyperspectral image classification, " IEEE Trans.
Geosci. Remote Sens., vol. 53, no. 11, pp. 6114-6133, 2015. doi:
10.1109/TGRS.2015.2432059.
[74]	 W. Li and Q. Du, " Laplacian regularized collaborative graph
for discriminant analysis of hyperspectral imagery, " IEEE Trans.
Geosci. Remote Sens., vol. 54, no. 12, pp. 7066-7076, 2016. doi:
10.1109/TGRS.2016.2594848.
[75]	 W. Li, J. Liu, and Q. Du, " Sparse and low-rank graph for discriminant analysis of hyperspectral imagery, " IEEE Trans.
Geosci. Remote Sens., vol. 54, no. 7, pp. 4094-4105, 2016. doi:
10.1109/TGRS.2016.2536685.
[76]	 L. Pan, H.-C. Li, Y.-J. Deng, F. Zhang, X.-D. Chen, and Q. Du,
" Hyperspectral dimensionality reduction by tensor sparse and
low-rank graph-based discriminant analysis, " Remote Sens., vol.
9, no. 5, p. 452, 2017. doi: 10.3390/rs9050452.
[77]	 K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf, " An
introduction to kernel-based learning algorithms, " IEEE Trans.
Neural Netw., vol. 12, no. 2, 2001. doi: 10.1109/72.914517.
[78]	 S. Gao, I. W.-H. Tsang, and L.-T. Chia, " Kernel sparse representation for image classification and face recognition, " in Proc. European Conf. Computer Vision, 2010, pp. 1-14. doi: 10.1007/9783-642-15561-1_1.
[79]	 S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, " Distributed optimization and statistical learning via the alternating
direction method of multipliers, " Found. Trends Mach. Learn.,
vol. 3, no. 1, pp. 1-122, Jan. 2011. doi: 10.1561/2200000016.
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