OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 17, Iss. 26 — Dec. 21, 2009
  • pp: 23823–23842

Hyperspectral agricultural mapping using Support Vector Machine-Based Endmember Extraction (SVM-BEE)

Anthony M. Filippi, Rick Archibald, Budhendra L. Bhaduri, and Edward A. Bright  »View Author Affiliations


Optics Express, Vol. 17, Issue 26, pp. 23823-23842 (2009)
http://dx.doi.org/10.1364/OE.17.023823


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Abstract

Extracting endmembers from remotely-sensed images of vegetated areas can present difficulties. In this research, we applied a recently-developed endmember-extraction algorithm based on Support Vector Machines to the problem of semi-autonomous estimation of vegetation endmembers from a hyperspectral image. This algorithm, referred to as Support Vector Machine-Based Endmember Extraction (SVM-BEE), accurately and rapidly yields a computed representation of hyperspectral data that can accommodate multiple distributions. The number of distributions is identified without prior knowledge, based upon this representation. Prior work established that SVM-BEE is robustly noise-tolerant and can semi-automatically estimate endmembers; synthetic data and a geologic scene were previously analyzed. Here we compared the efficacies of SVM-BEE, N-FINDR, and SMACC algorithms in extracting endmembers from a real, predominantly-agricultural scene. SVM-BEE estimated vegetation and other endmembers for all classes in the image, which N-FINDR and SMACC failed to do. SVM-BEE was consistent in the endmembers that it estimated across replicate trials. Spectral angle mapper (SAM) classifications based on SVM-BEE-estimated endmembers were significantly more accurate compared with those based on N-FINDR- and (in general) SMACC-endmembers. Linear spectral unmixing accrued overall accuracies similar to those of SAM.

© 2009 OSA

OCIS Codes
(100.0100) Image processing : Image processing
(100.2000) Image processing : Digital image processing
(200.4560) Optics in computing : Optical data processing
(280.0280) Remote sensing and sensors : Remote sensing and sensors

ToC Category:
Image Processing

History
Original Manuscript: July 6, 2009
Revised Manuscript: September 3, 2009
Manuscript Accepted: September 24, 2009
Published: December 14, 2009

Citation
Anthony M. Filippi, Rick Archibald, Budhendra L. Bhaduri, and Edward A. Bright, "Hyperspectral agricultural mapping using Support Vector Machine-Based Endmember Extraction (SVM-BEE)," Opt. Express 17, 23823-23842 (2009)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-17-26-23823


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