IEEE Geoscience and Remote Sensing Magazine - September 2021 - 111

[216] J. Verrelst, J. P. Rivera, A. Gitelson, J. Delegido, J. Moreno, and
G. Camps-Valls, " Spectral band selection for vegetation properties
retrieval using gaussian processes regression, " Int. J. Appl.
Earth Observ. Geoinf., vol. 52, pp. 554-567, 2016. doi: 10.1016/j.
jag.2016.07.016.
[217] S. T. Nahr, P. Pahlavani, and M. Hasanlou, " Different optimal
band selection of hyperspectral images using a continuous
genetic algorithm, " Int. Archives Photogrammetry, Remote Sens.
Spatial Inform. Sci., vol. XL-2/W3, no. 2, pp. 249, 2014. doi:
10.5194/isprsarchives-XL-2-W3-249-2014.
[218] X. Li and G. Wang, " Optimal band selection for hyperspectral
data with improved differential evolution, " J. Ambient Intell.
Humanized Comput., vol. 6, no. 5, pp. 675-688, 2015. doi:
10.1007/s12652-015-0285-8.
[219] M. Huber-Lerner, O. Hadar, S. R. Rotman, and R. HuberShalem,
" Hyperspectral band selection for anomaly detection:
The role of data gaussianity, " IEEE J. Sel. Topics Appl. Earth Observ.
Remote Sens., vol. 9, no. 2, pp. 732-743, 2015. doi: 10.1109/
JSTARS.2015.2487638.
[220] A. Lorencs, I. Mednieks, and J. Sinica-Sinavskis, " Selection of
informative bands for classification of hyperspectral images
based on entropy, " in Proc. 5th Biennial Baltic Electron. Conf.
(BEC), 2016, pp. 135-138. doi: 10.1109/BEC.2016.7743747.
[221] Y.-L. Chang, K.-S. Chen, B. Huang, W.-Y. Chang, J. A. Benediktsson,
and L. Chang, " A parallel simulated annealing approach
to band selection for high-dimensional remote sensing
images, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.,
vol. 4, no. 3, pp. 579-590, 2011. doi: 10.1109/JSTARS.2011.
2160048.
[222] S. A. Medjahed, T. A. Saadi, A. Benyettou, and M. Ouali, " A new
post-classification and band selection frameworks for hyperspectral
image classification, " Egyptian J. Remote Sens. Space Sci.,
vol. 19, no. 2, pp. 163-173, 2016. doi: 10.1016/j.ejrs.2016.09.003.
[223] F. Xie, C. Lei, F. Li, D. Huang, and J. Yang, " Unsupervised hyperspectral
feature selection based on fuzzy c-means and grey
wolf optimizer, " Int. J. Remote Sens., vol. 40, no. 9, pp. 3344-
3367, 2019. doi: 10.1080/01431161.2018.1541366.
[224] S. Li, Z. Zheng, Y. Wang, C. Chang, and Y. Yu, " A new hyperspectral
band selection and classification framework based on
combining multiple classifiers, " Pattern Recog. Lett., vol. 83,
pp. 152-159, Nov. 2016. doi: 10.1016/j.patrec.2016.05.013.
[225] Y. Xu, Q. Du, and N. H. Younan, " Particle swarm optimizationbased
band selection for hyperspectral target detection, " IEEE
Geosci. Remote Sens. Lett., vol. 14, no. 4, pp. 554-558, 2017. doi:
10.1109/LGRS.2017.2658666.
[226] D. B. Gillis, J. H. Bowles, and W. J. Moses, " Improving the retrieval
of water inherent optical properties in noisy hyperspectral
data through statistical modeling, " Opt. Express, vol. 21,
no. 18, pp. 21,306-21,316, 2013. doi: 10.1364/OE.21.021306.
[227] H. Su, B. Yong, and Q. Du, " Hyperspectral band selection
using improved firefly algorithm, " IEEE Geosci. Remote Sens.
Lett., vol. 13, no. 1, pp. 68-72, 2015. doi: 10.1109/LGRS.2015
.2497085.
[228] H. Su, Y. Cai, and Q. Du, " Firefly-algorithm-inspired framework
with band selection and extreme learning machine for
hyperspectral image classification, " IEEE J. Sel. Topics Appl.
SEPTEMBER 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
Earth Observ. Remote Sens., vol. 10, no. 1, pp. 309-320, 2016.
doi: 10.1109/JSTARS.2016.2591004.
[229] J. Tschannerl et al., " MIMR-DGSA: Unsupervised hyperspectral
band selection based on information theory and a modified
discrete gravitational search algorithm, " Inform. Fusion, vol. 51,
pp. 189-200, Nov. 2019. doi: 10.1016/j.inffus.2019.02.005.
[230] M. Wang, Y. Wan, Z. Ye, X. Gao, and X. Lai, " A band selection
method for airborne hyperspectral image based on chaotic binary
coded gravitational search algorithm, " Neurocomputing, vol.
273, pp. 57-67, Jan. 2018. doi: 10.1016/j.neucom.2017.07.059.
[231] U. P. Shukla and S. J. Nanda, " A binary social spider optimization
algorithm for unsupervised band selection in compressed
hyperspectral images, " Expert Syst. Appl., vol. 97, pp. 336-356,
May 2018. doi: 10.1016/j.eswa.2017.12.034.
[232] M. Gong, M. Zhang, and Y. Yuan, " Unsupervised band selection
based on evolutionary multiobjective optimization for
hyperspectral images, " IEEE Trans. Geosci. Remote Sens., vol. 54,
no. 1, pp. 544-557, 2015. doi: 10.1109/TGRS.2015.2461653.
[233] P. Hu, X. Liu, Y. Cai, and Z. Cai, " Band selection of hyperspectral
images using multiobjective optimization-based sparse
self-representation, " IEEE Geosci. Remote Sens. Lett., vol. 16,
no. 3, pp. 452-456, 2018. doi: 10.1109/LGRS.2018.2872540.
[234] P. Ghamisi and J. A. Benediktsson, " Feature selection based
on hybridization of genetic algorithm and particle swarm
optimization, " IEEE Geosci. Remote Sens. Lett., vol. 12, no. 2,
pp. 309-313, 2014. doi: 10.1109/LGRS.2014.2337320.
[235] Q. Chen, Y. Chen, and W. Jiang, " Genetic particle swarm optimization-based
feature selection for very-high-resolution
remotely sensed imagery object change detection, " Sensors,
vol. 16, no. 8, p. 1204, 2016. doi: 10.3390/s16081204.
[236] R. N. Patro, S. Subudhi, and P. K. Biswal, " Spectral clustering
and spatial Frobenius norm-based Jaya optimisation for
bs of hyperspectral images, " IET Image Process., vol. 13, no. 2,
pp. 307-315, Oct. 2018. doi: 10.1049/iet-ipr.2018.5109.
[237] L-l Mu, C-z Zhang, P-f Chi, and L. Liu, " A band selection
method of hyperspectral remote sensing based on particle frog
leaping algorithm, " Optoelectron. Lett., vol. 14, no. 4, pp. 316-
319, 2018. doi: 10.1007/s11801-018-8028-7.
[238] S. S. Sawant and P. Manoharan, " New framework for hyperspectral
band selection using modified wind-driven optimization
algorithm, " Int. J. Remote Sens., vol. 40, no. 20, pp. 1-22,
2019. doi: 10.1080/01431161.2019.1607609.
[239] L. Wang, H.-C. Li, B. Xue, and C.-I. Chang, " Constrained
band subset selection for hyperspectral imagery, " IEEE Geosci.
Remote Sens. Lett., vol. 14, no. 11, pp. 2032-2036, 2017. doi:
10.1109/LGRS.2017.2749209.
[240] B. J. Frey and D. Dueck, " Clustering by passing messages between
data points, " Science, vol. 315, no. 5814, pp. 972-976,
2007. doi: 10.1126/science.1136800.
[241] C. Pizzuti and S. E. Rombo, " An evolutionary restricted neighborhood
search clustering approach for PPI networks, " Neurocomputing,
vol. 145, pp. 53-61, Dec. 2014. doi: 10.1016/j.neucom
.2014.06.061.
[242] L. Zelnik-Manor and P. Perona, " Self-tuning spectral clustering, "
in Proc. Adv. Neural Inf. Process. Syst., 2005, pp. 1601-1608.
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IEEE Geoscience and Remote Sensing Magazine - September 2021

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