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Chinese Optics Letters

Chinese Optics Letters

| PUBLISHED MONTHLY BY CHINESE LASER PRESS AND DISTRIBUTED BY OSA

  • Vol. 9, Iss. 3 — Mar. 10, 2011
  • pp: 031002–

Hyperspectral remote sensing image classification based on decision level fusion

Peijun Du, Wei Zhang, and Junshi Xia  »View Author Affiliations


Chinese Optics Letters, Vol. 9, Issue 3, pp. 031002- (2011)


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Abstract

To apply decision level fusion to hyperspectral remote sensing (HRS) image classification, three decision level fusion strategies are experimented on and compared, namely, linear consensus algorithm, improved evidence theory, and the proposed support vector machine (SVM) combiner. To evaluate the effects of the input features on classification performance, four schemes are used to organize input features for member classifiers. In the experiment, by using the operational modular imaging spectrometer (OMIS) II HRS image, the decision level fusion is shown as an effective way for improving the classification accuracy of the HRS image, and the proposed SVM combiner is especially suitable for decision level fusion. The results also indicate that the optimization of input features can improve the classification performance.

© 2011 Chinese Optics Letters

OCIS Codes
(100.4145) Image processing : Motion, hyperspectral image processing
(280.4788) Remote sensing and sensors : Optical sensing and sensors

Citation
Peijun Du, Wei Zhang, and Junshi Xia, "Hyperspectral remote sensing image classification based on decision level fusion," Chin. Opt. Lett. 9, 031002- (2011)
http://www.opticsinfobase.org/col/abstract.cfm?URI=col-9-3-031002

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