References
- Benabadji, S.I., et al., 2019. Unsupervised hyperspectral band selection by combination of unmixing and sequential clustering techniques. European Journal of Remote Sensing, 52 (1), 30–39. doi:https://doi.org/10.1080/22797254.2018.1549511
- Bioucas-Dias, J.M. and Nascimento, J.M.P., 2008. Hyperspectral subspace identification. IEEE Transactions on Geoscience and Remote Sensing, 46 (8), 2435–2445. doi:https://doi.org/10.1109/TGRS.2008.918089
- Cao, X., et al., 2017. Hyperspectral band selection with objective image quality assessment. International Journal of Remote Sensing, 38 (12), 3656–3668. doi:https://doi.org/10.1080/01431161.2017.1302110
- Chandra, B. and Sharma, R.K. 2015. Exploring autoencoders for unsupervised feature selection. Paper presented at the 2015 International Joint Conference on Neural Networks (IJCNN), 12-17 July 2015, Killarney, Ireland.
- Chang, C.-I., et al., 1999. A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 37 (6), 2631–2641. doi:https://doi.org/10.1109/36.803411
- Chaudhary, Y., et al., 2021. YOLOv3 remote sensing SAR ship image detection. In: Lecture Notes on Data Engineering and Communications Technologies, vol 54. Springer, Singapore. https://doi.org/https://doi.org/10.1007/978-981-15-8335-3_40
- Du, P., et al. 2012. Target-driven change detection based on data transformation and similarity measures. Paper presented at the 2012 IEEE International Geoscience and Remote Sensing Symposium, 22-27 July 2012, Munich, Germany.
- Elkholy, M.M., et al., 2020a. Application of hyperspectral image unmixing for internet of things. In: Ghalwash, A.Z., El Khameesy, N., Magdi, D., Joshi, A. (Eds.) Internet of things—Applications and future. Springer, 249–260.
- Elkholy, M.M., et al., 2020b. Hyperspectral unmixing using deep convolutional autoencoder. International Journal of Remote Sensing, 41 (12), 4799–4819. doi:https://doi.org/10.1080/01431161.2020.1724346
- Ghamisi, P., Chen, Y., and Zhu, X.X., 2016. A self-improving convolution neural network for the classification of hyperspectral data. IEEE Geoscience and Remote Sensing Letters, 13 (10), 1537–1541. doi:https://doi.org/10.1109/LGRS.2016.2595108
- Han, K., et al. 2018. Autoencoder inspired unsupervised feature selection. Paper presented at the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 13 September 2018, Calgary, AB, Canada.
- Harsanyi, J.C. and Chang, C.-I., 1994. Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach. IEEE Transactions on Geoscience and Remote Sensing, 32 (4), 779–785. doi:https://doi.org/10.1109/36.298007
- Houghes, G., 1968. On the mean accuracy of statistical pattern recognition. IEEE Transactions on Information Theory, 14 (1), 55–63. doi:https://doi.org/10.1109/TIT.1968.1054102
- Huang, R. and He, M., 2005. Band selection based on feature weighting for classification of hyperspectral data. IEEE Geoscience and Remote Sensing Letters, 2 (2), 156–159. doi:https://doi.org/10.1109/LGRS.2005.844658
- Iordache, M.-D., Bioucas-Dias, J.M., and Plaza, A. 2015. Potential and limitations of band selection and library pruning in sparse hyperspectral unmixing. Paper presented at the 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2-5 June 2015, Tokyo, Japan.
- Jiang, S., Wang, Y., and Ji, Z., 2014. Convergence analysis and performance of an improved gravitational search algorithm. Applied Soft Computing, 24, 363–384. doi:https://doi.org/10.1016/j.asoc.2014.07.016
- Karoui, M.S., Djerriri, K., and Boukerch, I. 2020. Unsupervised hyperspectral band selection by sequentially clustering A Mahalanobis-based dissimilarity of spectrally variable endmembers. Paper presented at the 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), 9–11 March, 2020, Tunis, Tunisia.
- Khan, M.J., et al., 2018. Modern trends in hyperspectral image analysis: a review. IEEE Access, 6, 14118–14129. doi:https://doi.org/10.1109/ACCESS.2018.2812999
- Kim, B., and D. A. Landgrebe. 1990. Hierarchical classification in high-dimensional, numerous class cases. 10th Annual International Symposium on Geoscience and Remote Sensing, 1990, 2359–2362, doi: https://doi.org/10.1109/IGARSS.1990.689012
- Kingma, D.P. and Jimmy, B. 2014. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980.
- Li, Y. and Wu, H., 2012. A clustering method based on K-means algorithm. Physics Procedia, 25, 1104–1109. doi:https://doi.org/10.1016/j.phpro.2012.03.206
- Lorenzo, P.R., et al. 2018. “Band selection from hyperspectral images using attention-based convolutional neural networks.” arXiv preprint arXiv:1811.02667.
- Lorenzo, P.R., et al., 2020. Hyperspectral band selection using attention-based convolutional neural networks. IEEE Access, 8, 42384–42403. doi:https://doi.org/10.1109/ACCESS.2020.2977454
- Martinez-Uso, A., et al. 2006. Clustering-based multispectral band selection using mutual information. Paper presented at the 18th International Conference on Pattern Recognition (ICPR’06), 20-24 Aug. 2006, Hong Kong, China.
- MartÍnez-UsÓMartinez-Uso, A., et al., 2007. Clustering-based hyperspectral band selection using information measures. IEEE Transactions on Geoscience and Remote Sensing, 45 (12), 4158–4171. doi:https://doi.org/10.1109/TGRS.2007.904951
- Moustafa, M.S., Ahmed, S., and Hamed, A.A., 2020. Learning to hash with convolutional network for multi-label remote sensing image retrieval. International Journal of Intelligent Engineering and Systems, 13 (5), 539–548. doi:https://doi.org/10.22266/ijies2020.1031.47
- Noble, W.S., 2006. What is a support vector machine? Nature Biotechnology, 24 (12), 1565–1567. doi:https://doi.org/10.1038/nbt1206-1565
- Papa, J.P., et al. 2011. Feature selection through gravitational search algorithm. Paper presented at the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 22–27, 2011, Prague, Czech Republic.
- Patil, T., Pandey, S., and Visrani, K., 2021. A review on basic deep learning technologies and applications. In: Data science and intelligent applications. Singapore: Springer, 565–573.
- Patra, S., Modi, P., and Bruzzone, L., 2015. Hyperspectral band selection based on rough set. IEEE Transactions on Geoscience and Remote Sensing, 53 (10), 5495–5503. doi:https://doi.org/10.1109/TGRS.2015.2424236
- Peng, H., Long, F., and Ding, C., 2005. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (8), 1226–1238. doi:https://doi.org/10.1109/TPAMI.2005.159
- Rashedi, E. and Nezamabadi-pour, H., 2014. Feature subset selection using improved binary gravitational search algorithm. Journal of Intelligent & Fuzzy Systems, 26 (3), 1211–1221. doi:https://doi.org/10.3233/IFS-130807
- Rodarmel, C. and Shan, J., 2002. Principal component analysis for hyperspectral image classification. Surveying and Land Information Science, 62 (2), 115–122.
- Sefrin, O., Riese, F.M., and Keller, S., 2021. Deep Learning for Land Cover Change Detection. Remote Sensing, 13 (1), 78. doi:https://doi.org/10.3390/rs13010078
- Shi, H., Shen, Y., and Liu, Z. 2003. Hyperspectral bands reduction based on rough sets and fuzzy c-means clustering. Paper presented at the Proceedings of the 20th IEEE Instrumentation Technology Conference (Cat. No. 03CH37412), 20-22 May 2003, Vail, CO, USA.
- Sun, K., Geng, X., and Luyan, J., 2014. A new sparsity-based band selection method for target detection of hyperspectral image. IEEE Geoscience and Remote Sensing Letters, 12 (2), 329–333.
- Sun, W. and Du, Q., 2019. Hyperspectral band selection: a review. IEEE Geoscience and Remote Sensing Magazine, 7 (2), 118–139. doi:https://doi.org/10.1109/MGRS.2019.2911100
- Tane, Z., et al., 2018. Evaluating endmember and band selection techniques for multiple endmember spectral mixture analysis using post-fire imaging spectroscopy. Remote Sensing, 10 (3), 389. doi:https://doi.org/10.3390/rs10030389
- Wang, J. and Chang, C.-I., 2006. Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Transactions on Geoscience and Remote Sensing, 44 (6), 1586–1600. doi:https://doi.org/10.1109/TGRS.2005.863297
- Yang, H., et al., 2010. An efficient method for supervised hyperspectral band selection. IEEE Geoscience and Remote Sensing Letters, 8 (1), 138–142. doi:https://doi.org/10.1109/LGRS.2010.2053516
- Yuan, Y., Zhu, G., and Wang, Q., 2014. Hyperspectral band selection by multitask sparsity pursuit. IEEE Transactions on Geoscience and Remote Sensing, 53 (2), 631–644. doi:https://doi.org/10.1109/TGRS.2014.2326655
- Zabalza, J., et al., 2014. Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 112–122. doi:https://doi.org/10.1016/j.isprsjprs.2014.04.006
- Zhan, Y., et al., 2017. Hyperspectral band selection based on deep convolutional neural network and distance density. IEEE Geoscience and Remote Sensing Letters, 14 (12), 2365–2369. doi:https://doi.org/10.1109/LGRS.2017.2765339
- Zhang, C. and Liu, H. 2020. Dimensionality reduction of hyperspectral images based on subspace combination clustering and adaptive band selection. Paper presented at the MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 2-3 November 2019, Wuhan, China.
- Zhang, L., Zhang, L., and Du, B., 2016. Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4 (2), 22–40. doi:https://doi.org/10.1109/MGRS.2016.2540798
- Zhang, W., et al., 2018. A geometry-based band selection approach for hyperspectral image analysis. IEEE Transactions on Geoscience and Remote Sensing, 56 (8), 4318–4333. doi:https://doi.org/10.1109/TGRS.2018.2811046