Graph Embedding via High Dimensional Model Representation for Hyperspectral Images

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Taşkın Kaya G., Camps-Valls G.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, cilt.60, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 60
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1109/tgrs.2021.3133957
  • Dergi Adı: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), Business Source Elite, Business Source Premier, CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Geobase, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Anahtar Kelimeler: Hyperspectral imaging, Manifold learning, Germanium, Manifolds, Kernel, Laplace equations, Eigenvalues and eigenfunctions, Dimensionality reduction (DR), feature extraction, hyperspectral image (HSI) classification, manifold learning, manifold, out-of-sample (OOS) problem, spectral embedding, DISCRIMINANT-ANALYSIS, CLASSIFICATION, REDUCTION, EXTENSIONS, EIGENMAPS, FRAMEWORK
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Learning the manifold structure of remote sensing images is of paramount relevance for modeling and understanding processes, as well as encapsulating the high dimensionality in a reduced set of informative features for subsequent classification, regression, or unmixing. Manifold learning methods have shown excellent performance when dealing with hyperspectral image (HSI) analysis, but, unless specifically designed, they cannot provide an explicit embedding map readily applicable to out-of-sample (OOS) data. A common assumption to deal with the problem is that the transformation between the high-dimensional input space and the latent space (typically low) is linear. This is a particularly strong assumption, especially when dealing with HSIs due to the well-known nonlinear nature of the data. To address this problem, a manifold learning method based on high-dimensional model representation (HDMR) is proposed, which enables a nonlinear embedding function to project OOS samples into the latent space. The proposed method is compared to manifold learning methods along with their linear counterparts and achieves promising performance in terms of classification accuracy for a representative set of HSIs.