- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Journal of Electrical and Computer Engineering
Volume 2013 (2013), Article ID 908906, 2 pages
Algorithms for Multispectral and Hyperspectral Image Analysis
1Sensors and Electron Devices Directorate, US Army Research Laboratory, Adelphi, MD 20783, USA
2Department of Mathematics, Wake Forest University, Winston-Salem, NC 27106, USA
3Space Data Systems Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
4Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA
Received 28 November 2012; Accepted 28 November 2012
Copyright © 2013 Heesung Kwon et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- N. Keshava and N. J. F. Mustard, “Spectral unmixing,” IEEE Signal Processing Magazine, vol. 19, no. 1, pp. 44–57, 2002.
- B. Somers, G. P. Asner, L. Tits, and P. Coppin, “Endmember variability in Spectral Mixture Analysis: a review,” Remote Sensing of Environment, vol. 115, no. 7, pp. 1603–1616, 2011.
- J. M. Bioucas-Dias, A. Plaza, N. Dobigeon et al., “Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 2, pp. 354–379, 2012.
- G. Camps-Valls, L. Gomez-Chova, J. Muñoz-Marí, J. Vila-Francés, and J. Calpe-Maravilla, “Composite kernels for hyperspectral image classification,” IEEE Geoscience and Remote Sensing Letters, vol. 3, no. 1, pp. 93–97, 2006.
- D. W. J. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker, “Anomaly detection from hyperspectral imagery,” IEEE Signal Processing Magazine, vol. 19, no. 1, pp. 58–69, 2002.
- S. Matteoli, M. Diani, and G. Corsini, “A tutorial overview of anomaly detection in hyperspectral images,” IEEE Aerospace and Electronic Systems Magazine, vol. 25, no. 7, pp. 5–28, 2010.
- D. G. Manolakis and G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Processing Magazine, vol. 19, no. 1, pp. 29–43, 2002.
- D. A. Landgrebe, Theory Methods in Multispectral Remote Sensing, John Wiley & Sons, Hoboken, NJ, USA, 2003.
- M. Eismann, Hyperspectral Remote Sensing, vol. PM 210, SPIE Press Monograph, 2012.
- D. Snyder, J. Kerekes, I. Fairweather, R. Crabtree, J. Shive, and S. Hager, “Development of a web-based application to evaluate target finding algorithms,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS '08), vol. 2, pp. II915–II918, July 2008.