References for the Full Spectral Imaging Project


This page contains references for the project to demonstrate the feasibility of Full Spectral Imaging

Related Projects

Google Earth Engine - A planetary-scale platform for environmental data & analysis - Google Earth Engine brings together the world's satellite imagery—trillions of scientific measurements dating back more than 25 years—and makes it available online with tools for scientists, independent researchers, and nations to mine this massive warehouse of data to detect changes, map trends and quantify differences to the earth's surface.

The ‘Global Monitoring for Environment and Security’ (GMES) represents a concerted effort to bring data and information providers together with users, so they can better understand each other and make environmental and security-related information available to the people who need it through enhanced or new services.


Global Relief Technologies - Assist the emergency responders, humanitarian and reconstruction communities in their life-saving work by facilitating the collection of critical data from remote locations via a secure, integrated solution that provides for more effective evaluation of, and response to, crisis situations.


The National Ecological Observatory Network (NEON) is a continental-scale research platform for discovering and understanding the impacts of climate change, land-use change, and invasive species on ecology. NEON will gather long-term data on ecological responses of the biosphere to changes in land use and climate, and on feedbacks with the geosphere, hydrosphere, and atmosphere. NEON is a national observatory, not a collection of regional observatories. It will consist of distributed sensor networks and experiments, linked by advanced cyberinfrastructure to record and archive ecological data for at least 30 years. Using standardized protocols and an open data policy, NEON will gather essential data for developing the scientific understanding and theory required to manage the nation’s ecological challenges.



The Audio & Video Compression Page


Welcome to Direct Readout This Portal provides the Direct Readout (DR) community with easy access to Earth remote sensing data and technologies through shared information resources.
We achieve this by:
- Providing mission-specific information and free technologies to acquire and process Direct Broadcast Data.
- Introducing the user community to Direct Readout Systems Technologies.
- Providing users with a design template to receive, process and analyze their own Direct Readout Data.

HSCompress Hyperspectral data contains a large amount of redundant data, both in the spectral and spatial domains. Typical image compression algorithms (for example JPEG) compress only the spatial domain of an image. HSCompress compression exploits both spatial and spectral image redundancy to achieve high fidelity at small file sizes.

Hyperspectral Data Compression In the development of an automated approach to hyperspectral processing, ASIT has also discovered a very useful technique for spectral data compression. This technique combines the power of adaptively updated spectral basis functions with wavelet based spatial compression.

Hyperspectral Image Compression Using Three-Dimensional Wavelet Transformation

Innovations in Wavelet-Based Video Compression

ISO Spectral Analysis Package - ISAP. ISAP is designed to assist in post-pipeline processing of SWS and LWS data. It can also be applied to PHOT-S and CAM-CVF data, and data from practically any spectrometer. ISAP is publicly available and operates in the IDL environment.

Principal Component Analysis Methods

Spectral Analysis of Data. A collection of links to papers, tutorials, software, and sites containing information about spectral analysis of data, with special emphasis given to geophysical data.

Spectral Analysis of Data. Spectral Analysis is the analysis of data with respect to its spectral structure. By analysing the eigenvectors of a matrix, we can represent complex data sets approximately by a collection of points in a low dimensional space. This simplification filters error, and present a more lucid view of the data.

Video Compression Tutorial

Related Articles

"A Conceptual Design for an imaging Spectrometer" A study done by the author, completed in 1991, for the MODIS-T Project

IRAD FY04: Full Spectral Imaging Concept Development Proposal


1. Shannon, Claude E., and Weaver, Warren, The Mathematical Theory of Communication, University of Illinois Press, 1963.

2. Hyvarinen, L. P., Information Theory for Systems Engineers, Springer-Verlag, 1970.

3. Hankerson, Darrel; Harris, Greg A.; and Johnson. Peter D. Jr., Introduction to Information Theory and Data Compression, CRC Press, 1997.

4. Landgrebe, David, “Hyperspectral Image Data Analysis as a High Dimensional Signal Processing Problem”, IEEE Signal Processing Magazine, Vol. 19, No. 1 pp. 17-28, January 2002.

5. Van der Meer, F. & De Jong, S. “Imaging Spectrometry: Basic Principles and Prospective Applications”, Kluwer Academic Publishers, Dordrecht, the Netherlands, 451 pp. (ISBN 1-4020-0194-0).

6. Larar, Allen M.; Tong, Qingxi; Suzuki, Makoto, Multispectral and Hyperspectral Remote Sensing Instruments and Applications, Proceedings of SPIE Volume:4897, 2003.

7. Hall, F. G., Strebel, D. E., Nickeson, J. E., Goetz, S. J., “Radiometric rectification: Toward a common radiometric response among multidate, multisensor images”, Rem. Sens. Environ., 35:11-27 (1991).

8. Campbell, Norm, Furby, Suzanne Fergusson, Brian, “Calibrating Images from Different Dates”,

9. Schott, J. , C. Salvaggio and W. Volchok, "Radiometric Scene Normalization Using Pseudoinvariant Features", Remote Sensing of Environment 26:1-16 (1988).

10. Moran, M.S. Clarke, T.R. and Qi, J., Barnes, E.M., and Pinter, P.J. Jr., “Practical Techniques for conversion of airborne imagery to reflectances”,

11. Franz, Bryan, “OCTS, MOS, and POLDER Vicarious Calibration Analyses”, SIMBIOS Science Team Meeting 13 September 1999,

12. Teillet, P. M., Fedosejevs, G., Gauthier, R. P., O'Neill, N. T., Thome, Kurtis J., Biggar, Stuart F., Ripley, H., Meygret, A., “A Generalized Approach to the Vicarious Calibration of Multiple Earth Observation Sensors in Hyperspectral Data”, Remote Sensing of Environment, 77:3, p. 304.

13. Teillet Philippe M., Thome, Kurtis J., Fox, Nigel, and Morisette, Jeffrey T., “Earth Observation Sensor Calibration Using A Global Instrumented and Automated Network of Test Sites (GIANTS)”, (preprint).

14. Secker, J., Staenz, K., Gauthier, R.P., Budkewitsch, P., “Vicarious calibration of airborne hyperspectral sensors in operational environments”, ”, Remote Sensing of Environment 76, No. 1, April 2001, pp. 81-92.

15. Fougnie B., Deschamps P.Y., Frouin R., “Vicarious calibration of the POLDER ocean color spectral bands using in-situ measurements”, IEEE Transactions on Geoscience and Remote Sensing, Volume 37, Number 03, p. 1567, May 1999.

16. Subramanian, S., Gat, N., Ratcliff, A., Eismann, M., “Real-time Hyperspectral Data Compression Using Principal Components Transformation”,

17. Kaarna, A., Zemcik, P., Kälviäinen, H., and Parkkinen, J., “Compression of Multispectral Remote Sensing Images Using Clustering and Spectral Reduction”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 2, pp. 1073-1082, 2000.

18. X. Tang, W. A. Pearlman, and J. W. Modestino, "Hyperspectral Image Compression Using Three-Dimensional Wavelet Coding", SPIE/IS&T Electronic Imaging 2003, Proceedings of SPIE, Vol. 5022, Jan. 2003.

19. Qian, S.E., Hollinger, A.B., Williams, D., Manak, D., “Fast Three-Dimensional Data-Compression of Hyperspectral Imagery Using Vector Quantization with Spectral-Feature-Based Binary Coding”, Optical Engineering, 35, No. 11, pp. 3242-3249, November 1996.

20. Bolton, J., “A conceptual design for an imaging spectrometer”, NASA/GSFC Internal Technical Report, 1991

21. Reichenbach, S., Cao, L., and, Narayanan, R., “Information Efficiency in Hyperspectral Imaging Systems”, Journal of Electronic Imaging, 11(3):347-353, 2002.

22. Corner, B., Narayanan, R., and Reichenbach, S., “A Unified Model for the Information Content of Remote Sensing Imagery”, International Geoscience and Remote Sensing Symposium, IEEE, pp. 1807-1809, 2002.

23. Desetty, M., Narayanan, R., and Reichenbach, S., “Characterization of Information Content in Remote Sensing Imagery'', International Geoscience and Remote Sensing Symposium, pp. 2029-2031, 1998.

Sample Spectral Curves
Indiviual Spectral Reflectance Curves of the overstory species in LBL, Kentucky

Papers and Announcements




This page was last modified on Friday, January 14, 2011