Level set hyperspectral image segmentation using spectral information divergence-based best band selection

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn , Issue , 2007, Pages 4053-4056

  Ball, John E ^a,b   West, Terrance ^a,b   Prasad, Saurabh ^a,b   Bruce, Lori Mann ^a,b  

^a IEEE

^b MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Band analysis; Band selection; Bhattacharyya distance; Classification; Distance metrics; Hyperspectral; Image classification; Image processing; Image region analysis; Image segmentation; Level sets; Segmentation; SID; Spectral information divergence

Indexed keywords

BAND ANALYSIS; BAND SELECTION; BHATTACHARYYA DISTANCE; DISTANCE METRICS; HYPERSPECTRAL; IMAGE REGION ANALYSIS; SPECTRAL INFORMATION DIVERGENCE;

IMAGE CLASSIFICATION; IMAGE SEGMENTATION; MAXIMUM LIKELIHOOD;

SATELLITE IMAGERY;

EID: 78650919905     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2007.4423739     Document Type: Conference Paper

References (14)

1. G. Hughes, "On the mean aoouraoy of statistical pattern recognizers," IEEE Trans. on Information Theory, vol. 14, no. 1, pp. 55-63, 1968.
2. C.I. Chang, "An information-theoretio approaoh to speotral variability, similarity, and discrimination for hyperspectral image analysis," IEEE Trans. on Information Theory, vol. 46, no. 5, pp. 1927-1932, Aug. 2000.
3. S.S. Chiang, C.I. Chang, and I.W. Ginsberg, "Unsupervised target deteotion in hyperspeotral images using projection pursuit," IEEE Trans. on Geoscience and Remote Sensing, vol. 39, no. 7, pp. 1380-1391, Jul. 2001.
4. C. Kwan, B. Ayhan, G. Chen, W. Jing, J. Baohong, and I.C. Chein, "A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents," IEEE Trans. on Geoscience and Remote Sensing, vol. 44, no. 2, pp. 409-419, Feb. 2006.
5. S.A. Robila and A. Gershman, "Spectral matching accuracy in processing hyperspeotral data," Proc. of the Intl. Symposium on Signals, Circuits and Systems, vol. 1, pp. 163-166, Jul. 2005.
6. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, 2nd ed. New York: John Wiley & Sons, 2001.
7. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. San Francisco: Academic Press, 1990.
8. J. A. Sethian, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, 2nd ed. Cambridge, UK: Cambridge University Press, 2002.
9. T. Keaton and J. Brokish, "A level set method for the extraction of roads from multispeotral imagery," Proc. of the 31st Applied Imagery Pattern Recognition Workshop, pp. 141-147, Oot. 2002.
10. J.E. Ball and L.M. Bruce, "Level set segmentation of remotely sensed hyperspeotral images," Proc. of the IEEE Intl. Conference Geoscience and Remote Sensing (IGARSS), Anohorage, AL, vol. 8, pp. 5638-5642, Jul. 2005.
11. J.E. Ball and L.M. Bruce, "Accuracy analysis of hyperspeotral imagery olassifioation using level sets," Proc. of the 2006 American Society of Photogrammetry and Remote Sensing Annual Conference, Reno, NV, Apr. 2006.
12. J.E. Ball and L.M. Braoe, "Level Set Hyperspeotral Segmentation: NearOptimal Speed Funotions using Band Seleotion and Soaled Speotral Angle Mapper," Proc. of the 2006 IEEE Intl. Geoscience and Remote Sensing Symposium, pp. 2596-2600, Jul. 2006.
13. R.G. Congalton and K. Green, Assessing the Accuracy cf Remotely Sensed Data: Principles and Practices. New York, NY: Lewis Publishers, 1999.
14. "ITRES Ino. Compaot Airborne Speotrographio Imager," 2007. available: http://itres.com/cgi-bin/products.cgi.