Application of EOF's to Multispectral Imagery: Data Compression and Noise Detection for AVIRIS

IEEE Transactions on Geoscience and Remote Sensing

Volumn 32, Issue 1, 1994, Pages 25-34

  Hoffman, Ross N ^a   Johnson, Douglas W ^a  

^a ATMOSPHERIC AND ENVIRONMENTAL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA COMPRESSION; IMAGE COMPRESSION; MATHEMATICAL TRANSFORMATIONS; MEASUREMENT ERRORS; SIGNAL ENCODING; SIGNAL FILTERING AND PREDICTION; SPECTROMETERS; SPURIOUS SIGNAL NOISE;

AIRBORNE VISIBLE AND INFRA RED IMAGING SPECTROMETER (AVIRIS); EMPIRICAL ORTHOGONAL FUNCTIONS (EOFS); LOSSY COMPRESSION RATIOS; TRANSFORM ENCODING;

MULTISPECTRAL SCANNERS;

EID: 0028014988     PISSN: 01962892     EISSN: 15580644     Source Type: Journal    
DOI: 10.1109/36.285186     Document Type: Article

References (15)

1. A. A. Green, M. Berman, P. Switzher, and M. D. Craig, “A transformation for ordering multispectral data in terms of image quality with implications for noise removal,” IEEE Trans. Geosci. Remote Sensing, vol. 26, pp. 65–74, 1988.
2. J. B. Lee, S. Woddyatt, and M. Berman, “Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform,” IEEE Trans. Geosci. Remote Sensing, vol. 28, pp. 295–304, May 1990.
3. A. F. H. Goetz and M. Herring, “The high resolution imaging spectrometer (HIRIS) for Eos,” IEEE Trans. Geosci. Remote Sensing, vol. 27, pp. 136–144, 1989.
4. P. E. Ardanuy, D. Han, and V. V. Salomonson, “The moderate resolution imaging spectrometer (MODIS) science and data system requirements,” IEEE Trans. Geosci. Remote Sensing, vol. 29, pp. 75–88, 1991.
5. C.-CT. Chen and D.A. Landgrebe, “A spectral feature design system for the HIRIS/MODIS era,” IEEE Trans. Geosci. Remote Sensing, vol. 27, pp. 681–686, 1989.
6. R. C. Gonzalez and P. Wintz, Digital Image Processing, 2nd ed. Reading, MA: Addison-Wesley, 1987.
7. M. Rabbani and P. W. Jones, Digital Image Compression Techniques. Bellingham, WA: Soc. Photo-Optical Instrumentation Engineers, 1991.
8. A. Papoulis, Probability, Random Variables, and Stochastic Processes, New York: McGraw-Hill, 1984.
9. A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for earth remote sensing,” Science, vol. 228, pp. 1147–1153, 1985.
10. G. Vane, Ed., Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). A Description of the Sensor, Ground Data Processing Facility, Laboratory Calibration, and First Results, Jet Propulsion Lab., Pasadena, CA, Publ. 87–38, 1987.
11. B.-C. Gao and A. F. H. Goetz, “Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data,” J. Geophys. Res., vol. 95, no. D4, pp. 3549–3564, 1990.
12. B.-C. Gao and A. F. H. Goetz, “Cloud area determination from AVIRIS data using water vapor channels near 1 µm,” J. Geophys. Res., vol. 96, no. D2, pp. 2857–2864, 1991.
13. R. O. Green, Ed., Proc. Second Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, Jet Propulsion Lab., Pasadena, CA, Publ. 90–54, Nov. 15, 1990.
14. G. K. Wallace, “The JPEG still picture compression standard,” Commun. ACM, vol. 34, no. 4, pp. 30–44, 1991.
15. R. A. Becker, J. M. Chambers, and A. R. Wilks, The New S Language, A Programming Environment for Data Analysis and Graphics. Wadsworth and Brooks, 1988.