{"references": ["H. Huang, J. Li, and J. Liu, \"Enhanced semi-supervised local\nfisher discriminant analysis for face recognition,\" Future Generation\nComputer Systems, vol. 28, no. 1, pp. 244\u2013253, 2012.", "S. Chen and D. Zhang, \"Semisupervised dimensionality reduction\nwith pairwise constraints for hyperspectral image classification,\"\nGeoscience and Remote Sensing Letters, IEEE, vol. 8, no. 2, pp.\n369\u2013373, 2011.", "H. Huang and M. Yang, \"Dimensionality reduction of hyperspectral\nimages with sparse discriminant embedding,\" Geoscience and Remote\nSensing, IEEE Transactions on, vol. 53, no. 9, pp. 5160\u20135169, 2015.", "A. Radoi, R. Tanase, and M. Datcu, \"Semantic interpretation of\nmulti-level change detection in multi-temporal satellite images,\" in\nGeoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE\nInternational. IEEE, 2015, pp. 4157\u20134160.", "M. Iva\u02c7si\u00b4c-Kos, M. Pavli\u00b4c, and M. Mateti\u00b4c, \"Data preparation for\nsemantic image interpretation,\" in Information Technology Interfaces\n(ITI), 2010 32nd International Conference on. IEEE, 2010, pp.\n181\u2013186.", "W. Li, S. Prasad, J. E. Fowler, and L. M. Bruce, \"Locality-preserving\ndimensionality reduction and classification for hyperspectral image\nanalysis,\" Geoscience and Remote Sensing, IEEE Transactions on,\nvol. 50, no. 4, pp. 1185\u20131198, 2012.", "J. Khoder, R. Younes, and F. B. Ouezdou, \"Stability of dimensionality\nreduction methods applied on artificial hyperspectral images,\" in\nComputer Vision and Graphics. Springer, 2012, pp. 465\u2013474.", "J. Feng, L. Jiao, F. Liu, T. Sun, and X. Zhang, \"Unsupervised feature\nselection based on maximum information and minimum redundancy\nfor hyperspectral images,\" Pattern Recognition, vol. 51, pp. 295\u2013309,\n2016.", "J. M. Sotoca and F. Pla, \"Supervised feature selection by clustering\nusing conditional mutual information-based distances,\" Pattern\nRecognition, vol. 43, no. 6, pp. 2068\u20132081, 2010.\n[10] B. Guo, R. I. Damper, S. R. Gunn, and J. D. Nelson, \"A\nfast separability-based feature-selection method for high-dimensional\nremotely sensed image classification,\" Pattern Recognition, vol. 41,\nno. 5, pp. 1653\u20131662, 2008.\n[11] L. Zhang, C. Chen, J. Bu, and X. He, \"A unified feature and instance\nselection framework using optimum experimental design,\" Image\nProcessing, IEEE Transactions on, vol. 21, no. 5, pp. 2379\u20132388,\n2012.\n[12] S. B. Kim and P. Rattakorn, \"Unsupervised feature selection using\nweighted principal components,\" Expert systems with applications,\nvol. 38, no. 5, pp. 5704\u20135710, 2011.\n[13] C.-I. Chang and S. Wang, \"Constrained band selection for\nhyperspectral imagery,\" Geoscience and Remote Sensing, IEEE\nTransactions on, vol. 44, no. 6, pp. 1575\u20131585, 2006.\n[14] W. Jian, \"Unsupervised intrusion feature selection based on genetic\nalgorithm and fcm,\" in Information Engineering and Applications.\nSpringer, 2012, pp. 1005\u20131012.\n[15] M. Breaban and H. Luchian, \"A unifying criterion for unsupervised\nclustering and feature selection,\" Pattern Recognition, vol. 44, no. 4,\npp. 854\u2013865, 2011.\n[16] P. Mitra, C. Murthy, and S. K. Pal, \"Unsupervised feature selection\nusing feature similarity,\" IEEE transactions on pattern analysis and\nmachine intelligence, vol. 24, no. 3, pp. 301\u2013312, 2002.\n[17] A. MartI\u00b4nez-UsO\u00b4Martinez-Uso, F. Pla, J. M. Sotoca, and\nP. Garc\u00b4\u0131a-Sevilla, \"Clustering-based hyperspectral band selection\nusing information measures,\" IEEE Transactions on Geoscience and\nRemote Sensing, vol. 45, no. 12, pp. 4158\u20134171, 2007.\n[18] Y. Qian, F. Yao, and S. Jia, \"Band selection for hyperspectral imagery\nusing affinity propagation,\" IET Computer Vision, vol. 3, no. 4, pp.\n213\u2013222, 2009.\n[19] B.-C. Kuo, C.-H. Li, and J.-M. Yang, \"Kernel nonparametric weighted\nfeature extraction for hyperspectral image classification,\" Geoscience\nand Remote Sensing, IEEE Transactions on, vol. 47, no. 4, pp.\n1139\u20131155, 2009.\n[20] M. Sugiyama, \"Dimensionality reduction of multimodal labeled data\nby local fisher discriminant analysis,\" The Journal of Machine\nLearning Research, vol. 8, pp. 1027\u20131061, 2007.\n[21] P. Deepa and K. Thilagavathi, \"Feature extraction of hyperspectral\nimage using principal component analysis and folded-principal\ncomponent analysis,\" in Electronics and Communication Systems\n(ICECS), 2015 2nd International Conference on. IEEE, 2015, pp.\n656\u2013660.\n[22] L. Ding, P. Tang, and H. Li, \"Isomap-based subspace analysis for\nthe classification of hyperspectral data,\" in Geoscience and Remote\nSensing Symposium (IGARSS), 2013 IEEE International. IEEE, 2013,\npp. 429\u2013432.\n[23] S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin,\n\"Graph embedding and extensions: a general framework for\ndimensionality reduction,\" Pattern Analysis and Machine Intelligence,\nIEEE Transactions on, vol. 29, no. 1, pp. 40\u201351, 2007.\n[24] R. Ji, Y. Gao, R. Hong, Q. Liu, D. Tao, and X. Li, \"Spectral-spatial\nconstraint hyperspectral image classification,\" Geoscience and Remote\nSensing, IEEE Transactions on, vol. 52, no. 3, pp. 1811\u20131824, 2014.\n[25] H. Yuan and Y. Y. Tang, \"Learning with hypergraph for hyperspectral\nimage feature extraction,\" Geoscience and Remote Sensing Letters,\nIEEE, vol. 12, no. 8, pp. 1695\u20131699, 2015.\n[26] N. Renard and S. Bourennane, \"Dimensionality reduction based on tensor modeling for classification methods,\" IEEE Transactions on\nGeoscience and Remote Sensing, vol. 47, no. 4, pp. 1123\u20131131, 2009.\n[27] A. Sellami and I. R. Farah, \"High-level hyperspectral image\nclassification based on spectro-spatial dimensionality reduction,\"\nSpatial Statistics, vol. 16, pp. 103\u2013117, 2016.\n[28] R. Clark, \"Spectral library,\" https://speclab.cr.usgs.gov/spectral-lib.\nhtml/, 2007, (Online; accessed 19-September-2007)."]}

With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the semantic interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact, this work presents a dimensionality reduction approach that allows to overcome the different issues improving the semantic interpretation of HSI. Therefore, in order to preserve the spatial information, the Tensor Locality Preserving Projection (TLPP) has been applied to transform the original HSI. In the second step, knowledge has been extracted based on the adjacency graph to describe the different pixels. Based on the transformation matrix using TLPP, a weighted matrix has been constructed to rank the different spectral bands based on their contribution score. Thus, the relevant bands have been adaptively selected based on the weighted matrix. The performance of the presented approach has been validated by implementing several experiments, and the obtained results demonstrate the efficiency of this approach compared to various existing dimensionality reduction techniques. Also, according to the experimental results, we can conclude that this approach can adaptively select the relevant spectral improving the semantic interpretation of HSI.