Abstract
For hyperspectral classification, the existence of mixed pixels reduces the classification accuracy. To solve the problem, we apply the multi-label classification technique to hyperspectral classification. The focus of multi-label classification is to construct label-specific features. However, some algorithms do not consider the construction of label-specific features from multiple perspectives, resulting in that useful information is not selected. In this paper, we propose a new hyperspectral image multi-label classification algorithm based on the fusion of label-specific features. The algorithm constructs label-specific features from the three perspectives: distance information and linear representation information between instances, clustering information between bands, and then merges three feature subsets to obtain a new label feature space, making each label has highly discriminative features. Comprehensive experiments are conducted on three hyperspectral multi-label data sets. Comparison results with state-of-the-art algorithms validate the superiority of our proposed algorithm.
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References
Ghamisi, P., Maggiori, E., Li, S., et al.: New frontiers in spectral-spatial hyperspectral image classification: the latest advances based on mathematical morphology, Markov random fields, segmentation, sparse representation, and deep learning. IEEE Geosci. Remote Sens. Mag. 6(3), 10–43 (2018)
Hu, C., Xv, M., Fan, Y.: Hyperspectral image classification method combining fast guided filtering and spatial neighborhood information. In: 2020 International Conference on Computer Communication and Network Security (CCNS), pp. 55–58. IEEE (2020)
Hsu, P.H., Cheng, Y.Y.: Hyperspectral image classification via joint sparse representation. In: IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, pp. 2997–3000. IEEE (2019)
Fang, L., Li, S., Duan, W., et al.: Classification of hyperspectral images by exploiting spectral-spatial information of superpixel via multiple Kernels. IEEE Trans. Geosci. Remote Sens. 53(12), 6663–6674 (2015)
Zhu, X.X., Tuia, D., Mou, L., et al.: Deep learning in remote sensing: a comprehensive re-view and list of resources. IEEE Geosci. Remote Sens. Mag. 5(4), 8–36 (2017)
Zhang, M., Gong, M., Mao, Y., et al.: Unsupervised feature extraction in hyperspectral images based on Wasserstein generative adversarial network. IEEE Trans. Geosci. Remote Sens. 57(5), 2669–2688 (2018)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer, Boston, MA (2009). https://doi.org/10.1007/978-0-387-09823-4_34
Read, J., Pfahringer, B., Holmes, G., et al.: Classifier chains for multi-label classification. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 254–269. Springer, Berlin, Heidelberg (2009). https://doi.org/10.1007/s10994-011-5256-5
Boutell, M.R., Luo, J., Shen, X., et al.: Learning multi-label scene classification. Patt. Recogn. 37(9), 1757–1771 (2004)
Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Patt. Recogn. 40(7), 2038–2048 (2007)
Zhang, M.L., Wu, L.: Lift: multi-label learning with label-specific features. IEEE Trans. Pattern Anal. Mach. Intell. 37(1), 107–120 (2014)
Zhang, J.J., Fang, M., Li, X.: Multi-label learning with discriminative features for each label. Neurocomputing 154, 305–316 (2015)
He, Z.F., Yang, M.: Sparse and low-rank representation for multi-label classification. Appl. Intell. 49(5), 1708–1723 (2019)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surveys (CSUR) 31(3), 264–323 (1999)
Guo, Y., Chung, F., Li, G., et al.: Leveraging label-specific discriminant mapping features for multi-label learning. ACM Trans. Knowl. Disc. Data (TKDD) 13(2), 1–23 (2019)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embed-ding. Science 290(5500), 2323–2326 (2000)
Guan, Y., Li, W., Zhang, B., et al.: Multi-label classification by formulating label-specific features from simultaneous instance level and feature level. Appl. Intell. 51(6), 3375–3390 (2021)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 22(8), 888–905 (2000)
Zhu, F., Wang, Y., Fan, B., et al.: Effective spectral unmixing via robust representation and learning-based sparsity. arXiv preprint arXiv:1409.0685 (2014)
Zhu, F., Wang, Y., Fan, B., et al.: Spectral unmixing via data-guided sparsity. IEEE Trans. Image Process. 23(12), 5412–5427 (2014)
Zhu, F., Wang, Y., Xiang, S., et al.: Structured sparse method for hyperspectral unmixing. ISPRS J. Photogramm. Remote. Sens. 88, 101–118 (2014)
Huang, J., Li, G., Huang, Q., et al.: Learning label specific features for multi-label classification. In: 2015 IEEE International Conference on Data Mining. IEEE, pp. 181–190 (2015)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1–27 (2011)
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Zhang, J., Ding, P., Fang, S. (2021). Multi-label Classification of Hyperspectral Images Based on Label-Specific Feature Fusion. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13110. Springer, Cham. https://doi.org/10.1007/978-3-030-92238-2_19
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