1. [1] Rasti B, Hong D, Hang R, Ghamisi P, Kang X, Chanussot J, Benediktsson JA, "Feature extraction for hyperspectral imagery: The evolution from shallow to deep: Overview and toolbox", IEEE Geoscience and Remote Sensing Magazine, Vol. 8(4), pp. 60-88, 2020. [DOI:10.1109/MGRS.2020.2979764]

2. [2] B. Kumar, O. Dikshit, A. Gupta, and M. K. Singh, "Feature extraction for hyperspectral image classification: A review", International Journal of Remote Sensing, Vol. 41(16), pp. 6248-6287, 2020. [DOI:10.1080/01431161.2020.1736732]

3. [3] B.A. Beirami. and M. Mokhtarzade, "Spatial-spectral classification of hyperspectral images based on multiple fractal-based features", Geocarto International, 37(1), pp. 231-245. 2022. [DOI:10.1080/10106049.2020.1713232]

4. [4] Li S, Song W, Fang L, Chen Y, Ghamisi P, Benediktsson JA., "Deep learning for hyperspectral image classification: An overview", IEEE Transactions on Geoscience and Remote Sensing, 57(9), pp. 6690-6709. 2019. [DOI:10.1109/TGRS.2019.2907932]

5. [5] Paoletti ME, Haut JM, Plaza J, Plaza A., "Deep learning classifiers for hyperspectral imaging: A review", ISPRS Journal of Photogrammetry and Remote Sensing, 158, pp. 279-317, 2019. [DOI:10.1016/j.isprsjprs.2019.09.006]

6. [6] Y. Xu, B. Du, F. Zhang, L. Zhang, "Hyperspectral image classification via a random patches network", ISPRS journal of photogrammetry and remote sensing, Vol. 142, pp. 344-357, 2018. [DOI:10.1016/j.isprsjprs.2018.05.014]

7. [7] TH. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng, Y. Ma, "PCANet: A simple deep learning baseline for image classification?", IEEE Transactions on Image Processing, Vol. 24(12), pp. 5017-5032, 2015. [DOI:10.1109/TIP.2015.2475625]

8. [8] L. Fang, N. He, S. Li, A. J. Plaza, J. Plaza, "A new spatial-spectral feature extraction method for hyperspectral images using local covariance matrix representation", IEEE Transactions on Geoscience and Remote Sensing, Vol. 56(6), pp. 3534-3546, 2018. [DOI:10.1109/TGRS.2018.2801387]

9. [9] G. Zhao, N. Li, B. Tu, G. Zhang, W. He, "Density peak covariance matrix for feature extraction of hyperspectral image", IEEE Geoscience and Remote Sensing Letters, Vol. 17(3), pp. 534-538, 2019. [DOI:10.1109/LGRS.2019.2926396]

10. [10] N. He, ME. Paoletti, JM. Haut, L. Fang, S. Li, A. Plaza, J. Plaza, "Feature extraction with multiscale covariance maps for hyperspectral image classification", IEEE Transactions on Geoscience and Remote Sensing, Vol. 57(2), pp. 755-769, 2018. [DOI:10.1109/TGRS.2018.2860464]

11. [11] S.A. Ahmadi, and N. Mehrshad, "Spectral-spatial feature extraction method for hyperspectral images classification using multiscale superpixel and covariance map", Geocarto International, pp. 1-18, 2020. [DOI:10.1080/10106049.2020.1734874]

12. [12] He N, Fang L, Li S, Plaza J, Plaza A, "Skip-connected covariance network for remote sensing scene classification", IEEE Transactions on Neural Networks and Learning Systems, 31(5), pp. 1461-1474, 2019. [DOI:10.1109/TNNLS.2019.2920374]

13. [13] Zhang, J., Wang, L., Zhou, L., & Li, W, "Beyond covariance: Sice and kernel based visual feature representation", International Journal of Computer Vision, 1296(2), pp. 300-320, 2021. [DOI:10.1007/s11263-020-01376-1]

14. [14] L. Wang, J. Zhang, L. Zhou, C. Tang, W. Li. "Beyond covariance: Feature representation with nonlinear kernel matrices", Presented at Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile, 2015. [DOI:10.1109/ICCV.2015.519]

15. [15] AA. Green, M. Berman, P. Switzer, MD. 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(1), pp. 65-74, 1988. [DOI:10.1109/36.3001]

16. [16] http://lesun.weebly.com/hyperspectral-data-set.html.