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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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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