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Research Articles

Hyperspectral images classification based on multiple kernel learning using SWT and KMNF with few training samples

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References

  • Bioucas-Dias, J.M., et al., 2013. Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and Remote Sensing Magazine, 1 (2), 6–36. doi:10.1109/MGRS.2013.2244672
  • Chutia, D., et al., 2016. Hyperspectral remote sensing classifications: a perspective survey. Transactions in GIS, 20 (4), 463–490. doi:10.1111/tgis.12164
  • Cover, T.M., 1965. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, 3, 326–334. doi:10.1109/PGEC.1965.264137
  • Cui, B., et al., 2019. Hyperspectral image classification based on multiple kernel mutual learning. Infrared Physics & Technology, 99, 113–122. doi:10.1016/j.infrared.2019.04.004
  • Dalla Mura, M., et al., 2010. Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. IEEE Geoscience and Remote Sensing Letters, 8 (3), 542–546. doi:10.1109/LGRS.2010.2091253
  • Fowler, J.E., 2005. The redundant discrete wavelet transform and additive noise. IEEE Signal Processing Letters, 12 (9), 629–632. doi:10.1109/LSP.2005.853048
  • Gamba, P., 2004. A collection of data for urban area characterization. In: Paper presented at the IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA.
  • Gao, L., et al., 2017. Optimized kernel minimum noise fraction transformation for hyperspectral image classification. Remote Sensing, 9 (6), 548. doi:10.3390/rs9060548
  • Gharbia, R., et al., 2018. Multi-spectral and panchromatic image fusion approach using stationary wavelet transform and swarm flower pollination optimization for remote sensing applications. Future Generation Computer Systems, 88, 501–511. doi:10.1016/j.future.2018.06.022
  • Gönen, M. and Alpaydın, E., 2011. Multiple kernel learning algorithms. Journal of Machine Learning Research, 12, 2211–2268.
  • Green, A.A., et al., 1988. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 26 (1), 65–74. doi:10.1109/36.3001
  • Gu, Y., et al., 2012. Representative multiple kernel learning for classification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 50 (7), 2852–2865. doi:10.1109/tgrs.2011.2176341
  • Gu, Y., et al., 2014. Model selection and classification with multiple kernel learning for hyperspectral images via sparsity. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (6), 2119–2130. doi:10.1109/JSTARS.2014.2318181
  • Gu, Y., et al., 2015. Multiple kernel learning via low-rank nonnegative matrix factorization for classification of hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (6), 2739–2751. doi:10.1109/jstars.2014.2362116
  • Gu, Y., et al., 2016. Nonlinear multiple kernel learning with multiple-structure-element extended morphological profiles for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 54 (6), 3235–3247. doi:10.1109/tgrs.2015.2514161
  • Gu, Y., et al., 2017. Multiple kernel learning for hyperspectral image classification: a review. IEEE Transactions on Geoscience and Remote Sensing, 55 (11), 6547–6565. doi:10.1109/tgrs.2017.2729882
  • Hassanzadeh, S. and Karami, A., 2016. Compression and noise reduction of hyperspectral images using non-negative tensor decomposition and compressed sensing. European Journal of Remote Sensing, 49 (1), 587–598. doi:10.5721/EuJRS20164931
  • Hong, D., et al., 2020a. More diverse means better: multimodal deep learning meets remote-sensing imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 59, 4340–4354.
  • Hong, D., et al., 2020b. Invariant attribute profiles: a spatial-frequency joint feature extractor for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58 (6), 3791–3808. doi:10.1109/TGRS.2019.2957251
  • Islam, R., Ahmed, B., and Hossain, A., 2020. Feature reduction of hyperspectral image for classification. Journal of Spatial Science, 67, 1–21.
  • Jia, S., Shen, L., and Qingquan, L., 2014. Gabor feature-based collaborative representation for hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 53 (2), 1118–1129.
  • Jia, S., et al., 2021. A survey: deep learning for hyperspectral image classification with few labeled samples. Neurocomputing, 448, 179–204. doi:10.1016/j.neucom.2021.03.035
  • Jiang, T., et al., 2020. Discriminative reconstruction constrained generative adversarial network for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 58, 4666–4679.
  • Kang, X., Li, S., and Benediktsson, J.A., 2013. Spectral–spatial hyperspectral image classification with edge-preserving filtering. IEEE Transactions on Geoscience and Remote Sensing, 52 (5), 2666–2677. doi:10.1109/TGRS.2013.2264508
  • Kordi Ghasrodashti, E., Helfroush, M.S., and Danyali, H., 2017. A wavelet-based classification of hyperspectral images using Schroedinger eigenmaps. International Journal of Remote Sensing, 38 (12), 3608–3634. doi:10.1080/01431161.2017.1302108
  • Li, J., et al., 2014. Multiple feature learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 53 (3), 1592–1606. doi:10.1109/TGRS.2014.2345739
  • Li, W. and Du, Q., 2016. A survey on representation-based classification and detection in hyperspectral remote sensing imagery. Pattern Recognition Letters, 83, 115–123. doi:10.1016/j.patrec.2015.09.010
  • Liu, T., et al., 2016. Class-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 54 (12), 7351–7365. doi:10.1109/TGRS.2016.2600522
  • Melgani, F. and Bruzzone, L., 2004. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42 (8), 1778–1790. doi:10.1109/TGRS.2004.831865
  • Mohan, A., Sapiro, G., and Bosch, E., 2007. Spatially coherent nonlinear dimensionality reduction and segmentation of hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 4 (2), 206–210. doi:10.1109/LGRS.2006.888105
  • Prasad, S., et al., 2018. Foreword to the special issue on hyperspectral remote sensing and imaging spectroscopy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11 (4), 1019–1021.
  • Qian, Y., Minchao, Y., and Zhou, J., 2012. Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features. IEEE Transactions on Geoscience and Remote Sensing, 51 (4), 2276–2291. doi:10.1109/TGRS.2012.2209657
  • Rakotomamonjy, A., et al., 2008. SimpleMKL. Journal of Machine Learning Research, 9, 2491–2521.
  • Rodarmel, C. and Shan, J., 2002. Principal component analysis for hyperspectral image classification. Surveying and Land Information Science, 62 (2), 115–122.
  • Su, H., et al., 2020. Ensemble learning for hyperspectral image classification using tangent collaborative representation. IEEE Transactions on Geoscience and Remote Sensing, 58 (6), 3778–3790. doi:10.1109/TGRS.2019.2957135
  • Tarabalka, Y., et al., 2010. SVM-and MRF-based method for accurate classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 7 (4), 736–740. doi:10.1109/LGRS.2010.2047711
  • Tharwat, A., et al., 2017. Linear discriminant analysis: a detailed tutorial. AI Communications, 30 (2), 169–190. doi:10.3233/AIC-170729
  • Tu, B., et al., 2020. Hyperspectral classification with noisy label detection via superpixel-to-pixel weighting distance. IEEE Transactions on Geoscience and Remote Sensing, 58 (6), 4116–4131. doi:10.1109/tgrs.2019.2961141
  • Tuia, D., et al., 2010. Learning relevant image features with multiple-kernel classification. IEEE Transactions on Geoscience and Remote Sensing, 48 (10), 3780–3791. doi:10.1109/tgrs.2010.2049496
  • Wang, Q., Gu, Y., and Tuia, D., 2016. Discriminative multiple kernel learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 54 (7), 3912–3927. doi:10.1109/tgrs.2016.2530807
  • Xu, X., et al., 2019. Subpixel component analysis for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57 (8), 5564–5579. doi:10.1109/TGRS.2019.2900484
  • Zhan, T., et al., 2018. Hyperspectral classification via superpixel kernel learning-based low rank representation. Remote Sensing, 10 (10), 1639. doi:10.3390/rs10101639
  • Zhong, Z., et al., 2014. Discriminant tensor spectral–spatial feature extraction for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 12 (5), 1028–1032. doi:10.1109/LGRS.2014.2375188

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