1. [1] F. Mirzapour, and H. Ghassemian, "Improving hyperspectral image classification by combining spectral, texture, and shape features", International Journal of Remote Sensing, Vol. 36(4), pp. 1070-1096, 2015. DOI: 10.1080/01431161.2015.1007251 [ DOI:10.1080/01431161.2015.1007251] 2. [2] P. Duan, X. Kang, S. Li, P. Ghamisi, and J.A. Benediktsson, "Fusion of multiple edge-preserving operations for hyperspectral image classification", IEEE Transactions on Geoscience and Remote Sensing, Vol. 57(12), pp.10336-10349, 2019. [ DOI:10.1109/TGRS.2019.2933588] 3. [3] J. Peng, X. Jiang, N. Chen, and H. Fu, "Local adaptive joint sparse representation for hyperspectral image classification", Neurocomputing, Vol 334, pp.239-248, 2019 [ DOI:10.1016/j.neucom.2019.01.034] 4. [4] Y. Chen, Z. Lin, X. Zhao, G. Wang, and Y. Gu, "Deep learning-based classification of hyperspectral data", IEEE Journal of Selected topics in applied earth observations and remote sensing, Vol. 7(6), pp. 2094-2107, 2014. DOI: 10.1109/JSTARS.2014.2329330 [ DOI:10.1109/JSTARS.2014.2329330] 5. [5] Y. Chen, , X. Zhao, and X. Jia, "Spectral-spatial classification of hyperspectral data based on deep belief network", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8(6): pp. 2381-2392, 2015. DOI: 10.1109/JSTARS.2015.2388577 [ DOI:10.1109/JSTARS.2015.2388577] 6. [6] W. Hu, Y. Huang, L. Wei, F. Zhang, H. Li, "Deep convolutional neural networks for hyperspectral image classification", Journal of Sensors, 2015. DOI: 10.1155/2015/258619 [ DOI:10.1155/2015/258619] 7. [7] G. Zhao, G. Liu, L. Fang, B. Tu, and P. Ghamisi, "Multiple convolutional layers fusion framework for hyperspectral image classification", Neurocomputing, Vol.339, pp.149-160. 2019. [ DOI:10.1016/j.neucom.2019.02.019] 8. [8] X. Kang, C. Li, S. Li, H. Lin, "Classification of hyperspectral images by Gabor filtering based deep network", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11(4), pp. 1166-1178, 2017. DOI: 10.1109/JSTARS.2017.2767185 [ DOI:10.1109/JSTARS.2017.2767185] 9. [9] W. Zhao, S. Li, A. Li, B. Zhang, and Y. Li, "Hyperspectral images classification with convolutional neural network and textural feature using limited training samples", Remote sensing letters, Vol. 10(5), pp.449-458, 2019. [ DOI:10.1080/2150704X.2019.1569274] 10. [10] X. Cao, F. Zhou, L. Xu, D. Meng, Z. Xu, J. Paisley, "Hyperspectral image classification with Markov random fields and a convolutional neural network", IEEE Transactions on Image Processing, Vol. 27(5), pp. 2354-2367, 2018. DOI: 10.1109/TIP.2018.2799324 [ DOI:10.1109/TIP.2018.2799324] 11. [11] T. H. 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 [ DOI:10.1109/TIP.2015.2475625] 12. [12] 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 [ DOI:10.1016/j.isprsjprs.2018.05.014] 13. [13] Y. Sun, F. Fu, and L. Fan, "A Novel Hyperspectral Image Classification Pattern Using Random Patches Convolution and Local Covariance", Remote Sensing, Vol.11(16), pp.1954. 2019. [ DOI:10.3390/rs11161954] 14. [14] B. A. Beirami, and M. Mokhtarzade, "Spatial-Spectral Random Patches Network for Classification of Hyperspectral Images", Traitement du Signal, Vol. 36(5), pp.399-406, 2019. [ DOI:10.18280/ts.360504] 15. [15] Z. Wang, H. Hu, L. Zhang, J. H. Xue, "Discriminatively guided filtering (DGF) for hyperspectral image classification", Neurocomputing, Vol. 275, pp. 1981-1987, 2018. DOI: 10.1016/j.neucom.2017.10.046 [ DOI:10.1016/j.neucom.2017.10.046] 16. [16] M. Imani, and H. Ghassemian, "Two dimensional linear discriminant analyses for hyperspectral data", Photogrammetric Engineering & Remote Sensing, Vol. 81(10), pp. 777-786, 2015. DOI: 10.14358/PERS.81.10.777 [ DOI:10.14358/PERS.81.10.777] 17. [17] Y. Guo, , T. Hastie, and R. Tibshirani, "Regularized linear discriminant analysis and its application in microarrays", Biostatistics, Vol. 8(1), pp. 86-100, 2006. DOI: 10.1093/biostatistics/kxj035 [ DOI:10.1093/biostatistics/kxj035] 18. [18] F. Mirzapour, and H. Ghassemian, "Moment-based feature extraction from high spatial resolution hyperspectral images", International Journal of Remote Sensing, Vol. 37(6), pp. 1349-136, 2016. DOI: 10.1080/2150704X.2016.1151568 [ DOI:10.1080/2150704X.2016.1151568] 19. [19] B. Asghari Beirami, and M. Mokhtarzade, "SVM classification of hyperspectral images using the combination of spectral bands and Moran's I features", presented at IEEE 10th Iranian Conference on Machine Vision and Image Processing (MVIP), Isfahan, Iran, 2017. DOI: 10.1109/IranianMVIP.2017.8342334 [ DOI:10.1109/IranianMVIP.2017.8342334] 20. [20] J. Jiang, C. Chen, Y. Yu, X. Jiang, J. Ma, "Spatial-aware collaborative representation for hyperspectral remote sensing image classification", IEEE Geoscience and Remote Sensing Letters, Vol. 14(3), pp. 404-408. 2017. DOI: 10.1109/LGRS.2016.2645708 [ DOI:10.1109/LGRS.2016.2645708]
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