Spectral-spatial rotation forest for hyperspectral image classification

Junshi Xia (1) , Lionel Bombrun (1) , Yannick Berthoumieu (1) , Christian Germain (1) , Peijun Du (2)

1 IMS - Laboratoire de l'intégration, du matériau au système
2 NJU - Nanjing University

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.

Mots clés: Rotation Forest; Spectral-spatial; Classification; Hyperspectral

Domaines: Traitement du signal et de l'image [eess.SP]

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⟩