1. [1] B. B. Damodaran and R. R. Nidamanuri, "Dynamic Linear Classifier System for Hyperspectral Image Classification for Land Cover Mapping," IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 7, no. 6, pp. 2080-2093, Jun. 2014, doi: 10.1109/JSTARS.2013.2294857. [ DOI:10.1109/JSTARS.2013.2294857] 2. [2] R. Aghaee and M. Mokhtarzade, "Classification of Hyperspectral Images Using Subspace Projection Feature Space," IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 9, pp. 1803-1807, Sep. 2015, doi: 10.1109/LGRS.2015.2424911. [ DOI:10.1109/LGRS.2015.2424911] 3. [3] Y. Wei et al., "Applications of Hyperspectral Remote Sensing in Ground Object Identification and Classification," Advances in Remote Sensing, vol. 06, no. 03, pp. 201-211, 2017, doi: 10.4236/ars.2017.63015. [ DOI:10.4236/ars.2017.63015] 4. [4] S. B. Serpico and G. Moser, "Extraction of Spectral Channels From Hyperspectral Images for Classification Purposes," IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 2, pp. 484-495, Feb. 2007, doi: 10.1109/TGRS.2006.886177. [ DOI:10.1109/TGRS.2006.886177] 5. [5] A. Paul and N. Chaki, "Dimensionality Reduction Using Band Correlation and Variance Measure from Discrete Wavelet Transformed Hyperspectral Imagery," Annals of Data Science, vol. 8, no. 2, pp. 261-274, Jun. 2021, doi: 10.1007/s40745-019-00210-x. [ DOI:10.1007/s40745-019-00210-x] 6. [6] G. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Trans Inf Theory, vol. 14, no. 1, pp. 55-63, Jan. 1968, doi: 10.1109/TIT.1968.1054102. [ DOI:10.1109/TIT.1968.1054102] 7. [7] Y. Zhong and L. Zhang, "An Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 3, pp. 894-909, Mar. 2012, doi: 10.1109/TGRS.2011.2162589. [ DOI:10.1109/TGRS.2011.2162589] 8. [8] X. Jia, B.-C. Kuo, and M. M. Crawford, "Feature Mining for Hyperspectral Image Classification," Proceedings of the IEEE, vol. 101, no. 3, pp. 676-697, Mar. 2013, doi: 10.1109/JPROC.2012.2229082. [ DOI:10.1109/JPROC.2012.2229082] 9. [9] 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 Recognit Lett, vol. 83, pp. 152-159, Nov. 2016, doi: 10.1016/j.patrec.2016.05.013. [ DOI:10.1016/j.patrec.2016.05.013] 10. [10] S. A. Hosseini and H. Ghassemian, "Rational function approximation for feature reduction in hyperspectral data," Remote Sensing Letters, vol. 7, no. 2, pp. 101-110, Feb. 2016, doi: 10.1080/2150704X.2015.1101180. [ DOI:10.1080/2150704X.2015.1101180] 11. [11] Introduction to Statistical Pattern Recognition. Elsevier, 1990. doi: 10.1016/C2009-0-27872-X. [ DOI:10.1016/C2009-0-27872-X] 12. [12] H. Yang, Q. Du, H. Su, and Y. Sheng, "An Efficient Method for Supervised Hyperspectral Band Selection," IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 1, pp. 138-142, Jan. 2011, doi: 10.1109/LGRS.2010.2053516. [ DOI:10.1109/LGRS.2010.2053516] 13. [13] A. A. Green, M. Berman, P. Switzer, and M. D. Craig, "A transformation for ordering multispectral data in terms of image quality with implications for noise removal," IEEE Transactions on Geoscience and Remote Sensing, vol. 26, no. 1, pp. 65-74, 1988, doi: 10.1109/36.3001. [ DOI:10.1109/36.3001] 14. [14] S. H. Alizadeh Moghaddam, M. Mokhtarzade, and B. A. Beirami, "A feature extraction method based on spectral segmentation and integration of hyperspectral images," International Journal of Applied Earth Observation and Geoinformation, vol. 89, p. 102097, Jul. 2020, doi: 10.1016/j.jag.2020.102097. [ DOI:10.1016/j.jag.2020.102097] 15. [15] G. Baudat and F. Anouar, "Generalized Discriminant Analysis Using a Kernel Approach," Neural Comput, vol. 12, no. 10, pp. 2385-2404, Oct. 2000, doi: 10.1162/089976600300014980. [ DOI:10.1162/089976600300014980] 16. [16] Bor-Chen Kuo and D. A. Landgrebe, "Nonparametric weighted feature extraction for classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 5, pp. 1096-1105, May 2004, doi: 10.1109/TGRS.2004.825578. [ DOI:10.1109/TGRS.2004.825578] 17. [17] C. Lee and D. A. Landgrebe, "Feature extraction based on decision boundaries," IEEE Trans Pattern Anal Mach Intell, vol. 15, no. 4, pp. 388-400, Apr. 1993, doi: 10.1109/34.206958. [ DOI:10.1109/34.206958] 18. [18] J. A. Richards, Remote Sensing Digital Image Analysis. Cham: Springer International Publishing, 2022. doi: 10.1007/978-3-030-82327-6. [ DOI:10.1007/978-3-030-82327-6] 19. [19] M. Imani and H. Ghassemian, "Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples," IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 8, pp. 1325-1329, Aug. 2014, doi: 10.1109/LGRS.2013.2292892. [ DOI:10.1109/LGRS.2013.2292892] 20. [20] Bor-Chen Kuo, Cheng-Hsuan Li, and Jinn-Min Yang, "Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 4, pp. 1139-1155, Apr. 2009, doi: 10.1109/TGRS.2008.2008308. [ DOI:10.1109/TGRS.2008.2008308] 21. [21] A. Paul and N. Chaki, "Band selection using spectral and spatial information in particle swarm optimization for hyperspectral image classification," Soft comput, vol. 26, no. 6, pp. 2819-2834, Mar. 2022, doi: 10.1007/s00500-022-06821-6. [ DOI:10.1007/s00500-022-06821-6] 22. [22] B. Asghari Beirami and M. Mokhtarzade, "Band Grouping SuperPCA for Feature Extraction and Extended Morphological Profile Production From Hyperspectral Images," IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 11, pp. 1953-1957, Nov. 2020, doi: 10.1109/LGRS.2019.2958833. [ DOI:10.1109/LGRS.2019.2958833] 23. [23] N. Wambugu et al., "Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review," International Journal of Applied Earth Observation and Geoinformation, vol. 105, p. 102603, Dec. 2021, doi: 10.1016/j.jag.2021.102603. [ DOI:10.1016/j.jag.2021.102603] 24. [24] B. Mojaradi, H. Abrishami-Moghaddam, M. J. V. Zoej, and R. P. W. Duin, "Dimensionality Reduction of Hyperspectral Data via Spectral Feature Extraction," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 7, pp. 2091-2105, Jul. 2009, doi: 10.1109/TGRS.2008.2010346. [ DOI:10.1109/TGRS.2008.2010346] 25. [25] A. Ghorbanian and A. Mohammadzadeh, "An unsupervised feature extraction method based on band correlation clustering for hyperspectral image classification using limited training samples," Remote Sensing Letters, vol. 9, no. 10, pp. 982-991, Oct. 2018, doi: 10.1080/2150704X.2018.1500723. [ DOI:10.1080/2150704X.2018.1500723] 26. [26] B. Beirami and M. Mokhtarzade, "An Automatic Method for Unsupervised Feature Selection of Hyperspectral Images Based on Fuzzy Clustering of Bands," Traitement du Signal, vol. 37, no. 2, pp. 319-324, Apr. 2020, doi: 10.18280/ts.370218. [ DOI:10.18280/ts.370218] 27. [27] A. Ghorbanian, Y. Maghsoudi, and A. Mohammadzadeh, "Clustering-Based Band Selection Using Structural Similarity Index and Entropy for Hyperspectral Image Classification," Traitement du Signal, vol. 37, no. 5, pp. 785-791, Nov. 2020, doi: 10.18280/ts.370510. [ DOI:10.18280/ts.370510] 28. [28] J. C. Harsanyi, W. H. Farrand, and C.-I. Chang, "Determining the number and identity of spectral endmembers: an integrated approach using Neyman-Pearson eigen-thresholding and iterative constrained RMS error minimization," in Proceedings of the Thematic Conference on Geologic Remote Sensing, 1993, p. 395. 29. [29] J. M. P Nascimento and J. M. Bioucas-Dias, "Hyperspectral signal subspace estimation," in 2007 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2007, pp. 3225-3228. doi: 10.1109/IGARSS.2007.4423531. [ DOI:10.1109/IGARSS.2007.4423531] 30. [30] M. E. Winter, "N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data," in Imaging Spectrometry V, 1999, pp. 266-275. [ DOI:10.1117/12.366289] 31. [31] R. O. Duda, P. E. Hart, and others, Pattern classification. John Wiley & Sons, 2006. 32. [32] D. Chicco, M. J. Warrens, and G. Jurman, "The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen's Kappa and Brier Score in Binary Classification Assessment," IEEE Access, vol. 9, pp. 78368-78381, 2021, doi: 10.1109/ACCESS.2021.3084050. [ DOI:10.1109/ACCESS.2021.3084050] |
