R. P. Duin, A. L. Fred, M. Loog, E. Pekalska, G. L. Gimel'farb et al., Mode Seeking Clustering by KNN and Mean Shift Evaluated.," in, Proc. Structural and Syntactic Patt. Rec. and Stat.Tech. in Patt. Rec, vol.7626, pp.51-59, 2012.

C. Cariou and K. Chehdi, Nearest neighbor-density-based clustering methods for large hyperspectral images, Proceedings of SPIE -The International Society for Optical Engineering, p.10427, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01713367

M. Du, S. Ding, and H. Jia, Study on density peaks clustering based on k-nearest neighbors and principal component analysis, Knowledge-Based Systems, vol.99, pp.135-145, 2016.

T. N. Tran, R. Wehrens, and L. M. Buydens, KNN-kernel density-based clustering for high-dimensional multivariate data, Computational Statistics & Data Analysis, vol.51, issue.2, pp.513-525, 2006.

C. Cariou and K. Chehdi, A new k-nearest neighbor density-based clustering method and its application to hyperspectral images, pp.6161-6164, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01484548

H. Su, H. Yang, Q. Du, and Y. Sheng, Semisupervised band clustering for dimensionality reduction of hyperspectral imagery, IEEE Geoscience and Remote Sensing Letters, vol.8, issue.6, pp.1135-1139, 2011.

Y. Qian, F. Yao, and S. Jia, Band Selection for Hyperspectral Imagery Using Affinity Propagation, IET Computer Vision, vol.3, issue.4, pp.213-222, 2009.

B. J. Frey and D. Dueck, Clustering by Passing Messages Between Data Points, Science, vol.315, issue.5814, pp.972-976, 2007.

A. Rodriguez and A. Laio, Clustering by fast search and find of density peaks, Science, vol.344, issue.6191, pp.1492-1496, 2014.

G. Tang, S. Jia, L. , and J. , An enhanced density peak-based clustering approach for hyperspectral band selection, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.1116-1119, 2015.

C. I. Chang and S. Wang, Constrained band selection for hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.44, issue.6, pp.1575-1585, 2006.

C. Chang, S. Member, Q. Du, and S. Member, A Joint Band Prioritization and Band-Decorrelation Approach to Band Selection for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.37, issue.6, pp.2631-2641, 1999.

C. Cariou, K. Chehdi, L. Moan, and S. , BandClust: An unsupervised band reduction method for hyperspectral remote sensing, IEEE Geoscience and Remote Sensing Letters, vol.8, issue.3, pp.565-569, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00946927

S. C. Vermillion, R. ;. Raqueño, and R. Simmons, Spectral band characterization for hyperspectral monitoring of water quality, Proceedings of the Tenth JPL Airborne Earth Science Workshop, pp.435-443, 2001.

E. Belluco, M. Camuffo, S. Ferrari, L. Modenese, S. Silvestri et al., Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing, Remote Sensing of Environment, vol.105, issue.1, pp.54-67, 2006.

C. Cariou and K. Chehdi, Unsupervised nearest neighbors clustering with application to hyperspectral Images, IEEE Journal on Selected Topics in Signal Processing, vol.9, issue.6, pp.1105-1116, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01133648

Q. Du and H. Yang, Similarity-based unsupervised band selection for hyperspectral image analysis, IEEE Geoscience and Remote Sensing Letters, vol.5, issue.4, pp.564-568, 2008.

K. L. Smith, M. D. Steven, and J. J. Colls, Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks, Remote Sensing of Environment, vol.92, issue.2, pp.207-217, 2004.

, (a): Color composite (bands 30, 20, 10); (b): Reference map, 0500.

, Top: Average correct classification rate (ACCR), Comparison of NN-DB classification results (ModeSeek, knnDPC and GWENN-WM) without DR, and after applying GWENN-DR in band selection (BSel) vs. band averaging (BAvg)