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Home / Photogrammetric Engineering & Remote Sensing, Number 9 / September 2014

Open Access Spectral-Angle-based Laplacian Eigenmaps for Nonlinear Dimensionality Reduction of Hyperspectral Imagery

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In traditional manifold learning of hyperspectral imagery, distances among pixels are defined in terms of Euclidean distance, which is not necessarily the best choice because of its sensitivity to variations in spectrum magnitudes. Selecting Laplacian Eigenmaps (LE) as the test method, this paper studies the effects of distance metric selection in LE and proposes a spectral-angle-based LE method (LE-SA) to be compared against the traditional LE-based on Euclidean distance (LE-ED). LE-SA and LE-ED were applied to two airborne hyperspectral data sets and the dimensionality-reduced data were quantitatively evaluated. Experimental results demonstrated that LE-SA is able to suppress the variations within the same type of features, such as variations in vegetation and those in illuminations due to shade or orientations, and maintain a higher level of overall separability among different features than LE-ED. Further, the potential usage of a single LE-SA or LE-ED band for target detection is discussed.

Document Type: Research Article

Publication date: 01 September 2014

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  • The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.

    Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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