Optical Remote Sensing

Volume 3 of the series Augmented Vision and Reality pp 207-234


Exploring Nonlinear Manifold Learning for Classification of Hyperspectral Data

  • Melba M. CrawfordAffiliated withSchool of Civil Engineering and Department of Agronomy, Purdue University Email author 
  • , Li MaAffiliated withState Key Laboratory for Multi-spectral Information Processing Technologies, Huazhong University of Science and Technology
  • , Wonkook KimAffiliated withDepartment of Civil Engineering, Purdue University

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Increased availability of hyperspectral data and greater access to advanced computing have motivated development of more advanced methods for exploitation of nonlinear characteristics of these data. Advances in manifold learning developed within the machine learning community are now being adapted for analysis of hyperspectral data. This chapter investigates the performance of popular global (Isomap and KPCA) and local manifold nonlinear learning methods (LLE, LTSA, LE) for dimensionality reduction in the context of classification. Experiments were conducted on hyperspectral data acquired by multiple sensors at various spatial resolutions over different types of land cover. Nonlinear dimensionality reduction methods often outperformed linear extraction methods and rivaled or were superior to those obtained using the full dimensional data.


Manifold learning Dimensionality reduction Classification Hyperspectral Isometric feature mapping Kernel principal component analysis Locally linear embedding Local tangent space alignment Laplacian eigenmaps