IEEE Geoscience and Remote Sensing Magazine - December 2021 - 64

[65] Y. Zhong, A. Ma, and L. Zhang, " An adaptive memetic fuzzy
clustering algorithm with spatial information for remote sensing
imagery, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7,
no. 4, pp. 1235-1248, 2014. doi: 10.1109/JSTARS.2014.2303634.
[66] G. Bilgin, S. Erturk, and T. Yildirim, " Unsupervised classification
of hyperspectral-image data using fuzzy approaches
that spatially exploit membership relations, " IEEE Geosci. Remote
Sens. Lett., vol. 5, no. 4, pp. 673-677, 2008. doi: 10.1109/
LGRS.2008.2002319.
[67] S. Krinidis and V. Chatzis, " A robust fuzzy local information Cmeans
clustering algorithm, " IEEE Trans. Image Process., vol. 19,
no. 5, pp. 1328-1337, 2010. doi: 10.1109/TIP.2010.2040763.
[68] H. Zhang, Q. Wang, W. Shi, and M. Hao, " A novel adaptive
fuzzy local information c-means clustering algorithm for remotely
sensed imagery classification, " IEEE Trans. Geosci. Remote
Sens., vol. 55, no. 9, pp. 5057-5068, 2017. doi: 10.1109/
TGRS.2017.2702061.
[69] H. Zhang, L. Bruzzone, W. Shi, M. Hao, and Y. Wang, " Enhanced
spatially constrained remotely sensed imagery classification
using a fuzzy local double neighborhood information c-means
clustering algorithm, " IEEE J. Sel. Topics Appl. Earth Observ. Remote
Sens., vol. 11, no. 8, pp. 2896-2910, 2018. doi: 10.1109/
JSTARS.2018.2846603.
[70] A. K. Jain, " Data clustering: 50 years beyond K-means, " Pattern
Recognit. Lett., vol. 31, no. 8, pp. 651-666, 2010. doi: 10.1016/j.
patrec.2009.09.011.
[71] A. Rodriguez and A. Laio, " Clustering by fast search and find
of density peaks, " Science, vol. 344, no. 6191, pp. 1492-1496,
2014. doi: 10.1126/science.1242072.
[72] Y. Chen, S. Ma, X. Chen, and P. Ghamisi, " Hyperspectral data clustering
based on density analysis ensemble, " Remote Sens. Lett., vol. 8,
no. 2, pp. 194-203, 2017. doi: 10.1080/2150704X.2016.1249295.
[73] H. Bäcklund, A. Hedblom, and N. Neijman, " A density-based
spatial clustering of application with noise, " Data Min. TNM,
vol. 33, pp. 11-30, Nov. 2011.
[74] S. Jia, G. Tang, J. Zhu, and Q. Li, " A novel ranking-based clustering
approach for hyperspectral band selection, " IEEE Trans.
Geosci. Remote Sens., vol. 54, no. 1, pp. 88-102, 2015. doi:
10.1109/TGRS.2015.2450759.
[75] D. Comaniciu and P. Meer, " Mean shift: A robust approach toward
feature space analysis, " IEEE Trans. Pattern Anal. Mach. Intell.,
vol. 24, no. 5, pp. 603-619, 2002. doi: 10.1109/34.1000236.
[76] X. Huang and L. Zhang, " An adaptive mean-shift analysis approach
for object extraction and classification from urban hyperspectral
imagery, " IEEE Trans. Geosci. Remote Sens., vol. 46,
no. 12, pp. 4173-4185, 2008. doi: 10.1109/TGRS.2008.2002577.
[77] J. M. Murphy and M. Maggioni, " Unsupervised clustering and
active learning of hyperspectral images with nonlinear diffusion, "
IEEE Trans. Geosci. Remote Sens., vol. 57, no. 3, pp. 1829-
1845, 2018. doi: 10.1109/TGRS.2018.2869723.
[78] J. M. Murphy and M. Maggioni, " Spectral-spatial diffusion geometry
for hyperspectral image clustering, " IEEE Geosci. Remote
Sens. Lett., vol. 17, no. 7, pp. 1243-1247, 2020. doi: 10.1109/
LGRS.2019.2943001.
[79] N. Acito, G. Corsini, and M. Diani, " An unsupervised algorithm
for hyperspectral image segmentation based on the
64
Gaussian mixture model, " in Proc. IEEE Int. Geosci. Remote Sens.
Symp. (IGARSS), July 2003, vol. 6, pp. 3745-3747.
[80] C. A. Shah, M. K. Arora, and P. K. Varshney, " Unsupervised
classification of hyperspectral data: An ICA mixture model
based approach, " Int. J. Remote Sens., vol. 25, no. 2, pp. 481-
487, 2004. doi: 10.1080/01431160310001618040.
[81] C. A. Shah, P. K. Varshney, and M. K. Arora, " ICA mixture model
algorithm for unsupervised classification of remote sensing
imagery, " Int. J. Remote Sens., vol. 28, no. 8, pp. 1711-1731,
2007. doi: 10.1080/01431160500462121.
[82] C. F. Li, L. Liu, Y. M. Lei, J. Y. Yin, J. J. Zhao, and X. K. Sun,
" Clustering for HSI hyperspectral image with weighted PCA
and ICA, " J. Intell. Fuzzy Syst., vol. 32, no. 5, pp. 3729-3737,
2017. doi: 10.3233/JIFS-169305.
[83] G. Celeux, " The SEM algorithm: A probabilistic teacher algorithm
derived from the EM algorithm for the mixture problem, "
Comput. Statist. Quart., vol. 2, pp. 73-82, Jan. 1985.
[84] J. B. Courbot, V. Mazet, E. Monfrini, and C. Collet, " Pairwise
Markov fields for segmentation in astronomical hyperspectral
images, " Signal Process., vol. 163, pp. 41-48, Oct. 2019. doi:
10.1016/j.sigpro.2019.05.005.
[85] S. D. Xenaki, K. D. Koutroumbas, A. A. Rontogiannis, and O.
A. Sykioti, " A layered sparse adaptive possibilistic approach for
hyperspectral image clustering, " in Proc. IEEE Geosci. Remote
Sens. Symp. (IGARSS), July 2014, pp. 2890-2893. doi: 10.1109/
IGARSS.2014.6947080.
[86] C. Teodor, B. Alzenk, R. Constantinescu, and M. Datcu, " Unsupervised
classification of EO-1 Hyperion hyperspectral
data using Latent Dirichlet allocation, " in Proc. IEEE Int. Symp.
Signals Circuits Syst. (ISSCS), July 2013, pp. 1-4. doi: 10.1109/
ISSCS.2013.6651211.
[87] Y. Fang, L. Xu, J. Peng, H. Yang, A. Wong, and D. A. Clausi,
" Unsupervised Bayesian classification of a hyperspectral image
based on the spectral mixture model and Markov random field, "
IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 11, no. 9,
pp. 3325-3337, 2018. doi: 10.1109/JSTARS.2018.2858008.
[88] A. Baraldi and F. Parmiggiani, " A neural network for unsupervised
categorization of multivalued input patterns: An
application to satellite image clustering, " IEEE Trans. Geosci.
Remote Sens., vol. 33, no. 2, pp. 305-316, Mar. 1995. doi:
10.1109/36.377930.
[89] Y. Zhong, L. Zhang, and W. Gong, " Unsupervised remote sensing
image classification using an artificial immune network, "
Int. J. Remote Sens., vol. 32, no. 19, pp. 5461-5483, 2011. doi:
10.1080/01431161.2010.502155.
[90] J. Xu, H. Li, P. Liu, and L. Xiao, " A novel hyperspectral image clustering
method with context-aware unsupervised discriminative
extreme learning machine, " IEEE Access, vol. 6, pp. 16,176-
16,188, Mar. 2018. doi: 10.1109/ACCESS.2018.2813988.
[91] H. H. Muhammed, " Unsupervised hyperspectral image segmentation
using a new class of neuro-fuzzy systems based on
weighted incremental neural networks, " in Proc. IEEE Applied
Imagery Pattern Recognit. Workshop (AIPR), Oct. 2002, pp. 171-
177. doi: 10.1109/AIPR.2002.1182272.
[92] S. Das, A. Abraham, and A. Konar, " Automatic clustering using
an improved differential evolution algorithm, " IEEE Trans.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE DECEMBER 2021
IEEE Geoscience and Remote Sensing Magazine - December 2021

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - December 2021
