Skip to Main content Skip to Navigation
Conference papers

Spectral-spatial rotation forest for hyperspectral image classification

Abstract : Rotation Forest (RoF) is a decision tree ensemble classifier, which uses random feature selection and data transformation techniques to improve both the diversity and accuracy of base classifiers. Traditional RoF only considers data transformation on spectral information. In order to further improve the performance of RoF, we introduce spectral-spatial data transformation into RoF and thus propose a spectral-spatial Rotation Forest (SSRoF). The proposed method is experimentally investigated on a hyperspectral remote sensing image collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. Experimental results indicate that the proposed methodology achieves excellent performance.
Document type :
Conference papers
Complete list of metadata

Cited literature [14 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01379973
Contributor : Lionel Bombrun Connect in order to contact the contributor
Submitted on : Wednesday, October 12, 2016 - 11:31:57 AM
Last modification on : Tuesday, December 8, 2020 - 10:05:17 AM
Long-term archiving on: : Saturday, February 4, 2017 - 7:51:02 PM

File

Xia16_IGARSS.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01379973, version 1

Citation

Junshi Xia, Lionel Bombrun, Yannick Berthoumieu, Christian Germain, Peijun Du. Spectral-spatial rotation forest for hyperspectral image classification. IEEE International Geosicence and Remote Sensing Symposium (IGARSS 2016), Jul 2016, Pékin, China. ⟨hal-01379973⟩

Share

Metrics

Record views

198

Files downloads

363