Piecewise flat embeddings for hyperspectral image analysis

Tyler L. Hayes; Renee T. Meinhold; John F. Hamilton Jr.; Nathan D. Cahill

doi:10.1117/12.2262302

5 May 2017 Piecewise flat embeddings for hyperspectral image analysis

Tyler L. Hayes, Renee T. Meinhold, John F. Hamilton Jr., Nathan D. Cahill

Proceedings Volume 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII; 101980O (2017) https://doi.org/10.1117/12.2262302
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

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.

Conference Presentation

Citation Download Citation

Tyler L. Hayes, Renee T. Meinhold, John F. Hamilton Jr., and 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); https://doi.org/10.1117/12.2262302

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