Abstract
Cross-scene hyperspectral image (HSI) classification has recently become increasingly popular due to its crucial use in various applications. It poses great challenges to existing domain adaptation methods because of the data set shift, that is, two scenes exhibit huge distribution discrepancy. To tackle this problem, we propose a new domain adaptation method called Unsupervised Global Manifold Alignment (UGMA) for cross-scene HSI classification. The proposed UGMA method learns a common subspace by introducing two different projection matrices to extract the transferable knowledge from the source domain to the target domain. Specifically, UGMA takes the advantages of manifold learning that reduces the dimensionality and preserves the geometry structure. What’s more, in UGMA, we define a global geometry preserving term to deal with the situation where the global manifold geometry needs to be respected.
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References
Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: Advances in Neural Information Processing Systems, pp. 137–144 (2007)
Bruzzone, L., Prieto, D.F.: Unsupervised retraining of a maximum likelihood classifier for the analysis of multitemporal remote sensing images. IEEE Trans. Geosci. Remote Sens. 39(2), 456–460 (2001)
Chen, C., Li, W., Tramel, E.W., Cui, M., Prasad, S., Fowler, J.E.: Spectral-spatial preprocessing using multihypothesis prediction for noise-robust hyperspectral image classification. IEEE J. Sel. Topics Appl. Earth Observations Remote Sens. 7(4), 1047–1059 (2014)
Damodaran, B.B., Nidamanuri, R.R.: Dynamic linear classifier system for hyperspectral image classification for land cover mapping. IEEE J. Selected Topics Appl. Earth Observations Remote Sens. 7(6), 2080–2093 (2014)
Kang, X., Li, S., Benediktsson, J.A.: Feature extraction of hyperspectral images with image fusion and recursive filtering. IEEE Trans. Geosci. Remote Sens. 52(6), 3742–3752 (2014)
Kim, W., Crawford, M.M.: Adaptive classification for hyperspectral image data using manifold regularization kernel machines. IEEE Trans. Geosci. Remote Sens. 48(11), 4110–4121 (2010)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Ramzi, P., Samadzadegan, F., Reinartz, P.: Classification of hyperspectral data using an adaboostsvm technique applied on band clusters. IEEE J. Selected Topics Appl. Earth Observations Remote Sens. 7(6), 2066–2079 (2014)
Sun, Z., Wang, C., Li, P., Wang, H., Li, J.: Hyperspectral image classification with SVM-based domain adaption classifiers. In: 2012 International Conference on Computer Vision in Remote Sensing, pp. 268–272. IEEE (2012)
Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 40(5), 1267–1279 (2010)
Tuia, D., Pasolli, E., Emery, W.J.: Using active learning to adapt remote sensing image classifiers. Remote Sens. Environ. 115(9), 2232–2242 (2011)
Tuia, D., Pasolli, E., Emery, W.J.: Dataset shift adaptation with active queries. In: 2011 Joint Urban Remote Sensing Event, pp. 121–124. IEEE (2011)
Voisin, A., Krylov, V.A., Moser, G., Serpico, S.B., Zerubia, J.: Supervised classification of multisensor and multiresolution remote sensing images with a hierarchical copula-based approach. IEEE Trans. Geosci. Remote Sens. 52(6), 3346–3358 (2014)
Yang, J., Zhou, Y., Cao, Y., Feng, L.: Heterogeneous image change detection using deep canonical correlation analysis. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2917–2922. IEEE (2018)
Yang, L., Yang, S., Jin, P., Zhang, R.: Semi-supervised hyperspectral image classification using spatio-spectral laplacian support vector machine. IEEE Geosci. Remote Sens. Lett. 11(3), 651–655 (2014)
Yuan, Z., Feng, L., Hou, C., Kung, S.Y.: Hyperspectral and multispectral image fusion based on local low rank and coupled spectral unmixing. IEEE Trans. Geosci. Remote Sens. 55(10), 5997–6009 (2017)
Zhong, Y., Zhang, L.: An adaptive artificial immune network for supervised classification of multi-/hyperspectral remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 50(3), 894–909 (2012)
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Feng, W., Zhou, Y., Jin, D. (2019). Unsupervised Global Manifold Alignment for Cross-Scene Hyperspectral Image Classification. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_46
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DOI: https://doi.org/10.1007/978-3-030-31723-2_46
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