5 May 2017 Piecewise flat embeddings for hyperspectral image analysis
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Abstract
Graph-based dimensionality reduction techniques such as Laplacian Eigenmaps (LE), Local Linear Embedding (LLE), Isometric Feature Mapping (ISOMAP), and Kernel Principal Components Analysis (KPCA) have been used in a variety of hyperspectral image analysis applications for generating smooth data embeddings. Recently, Piecewise Flat Embeddings (PFE) were introduced in the computer vision community as a technique for generating piecewise constant embeddings that make data clustering / image segmentation a straightforward process. In this paper, we show how PFE arises by modifying LE, yielding a constrained ℓ1-minimization problem that can be solved iteratively. Using publicly available data, we carry out experiments to illustrate the implications of applying PFE to pixel-based hyperspectral image clustering and classification.
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Tyler L. Hayes, Tyler L. Hayes, Renee T. Meinhold, Renee T. Meinhold, John F. Hamilton, John F. Hamilton, Nathan D. Cahill, Nathan D. Cahill, } "Piecewise flat embeddings for hyperspectral image analysis", Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980O (5 May 2017); doi: 10.1117/12.2262302; https://doi.org/10.1117/12.2262302
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