Hyperspectral remote sensing image classification based on the integration of support vector machine and random forest

Peijun Du (1, 2) , Junshi Xia (3, 2) , Jocelyn Chanussot (3) , Xiyan He (3)

1 NJU - Nanjing University
2 CUMT - China University of Mining and Technology
3 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique

Support vector machine (SVM) and Random Forest (RF) have been developed to improve the accuracy of hyperspectral remote sensing (HRS) image classification significantly in recent years. Due to the different characteristics and obvious diversity between SVM and RF, we propose two integration approaches which combine SVM and Random Forest to classify the HRS image. The proposed method called DWDCS is examined by two hyperspectral images and it can acquire the higher overall accuracy and also improve the accuracy of each classes. Experimental results indicate that the proposed approaches have a great deal of advantages in classifying HRS image.

Mots clés: Hyperspectral Remote Sensing Image; Classifier ensemble; SVM; Random Forest

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

Peijun Du, Junshi Xia, Jocelyn Chanussot, Xiyan He. Hyperspectral remote sensing image classification based on the integration of support vector machine and random forest. IGARSS 2012 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2012, Munich, Germany. pp.174-177, ⟨10.1109/IGARSS.2012.6351609⟩. ⟨hal-00799693⟩