Land-cover classification using GF-2 images and airborne lidar data based on Random Forest

ABSTRACTLand cover and its change are one of the important factors of global environmental change. The new GaoFen-2 (GF-2) satellite provides abundant spectral features and texture information, and airborne light detection and ranging (lidar) provides accurate three-dimensional coordinates at a finer scale. Fusing these data has the potential to improve land-cover classification. In the article, we selected the Random Forest (RF) as a classifier. The spectral bands of GF-2, normalized difference vegetation index (NDVI), normalized digital surface model derived from lidar data, and their grey-level co-occurrence matrix (GLCM) textures including mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation were generated to create seven scenarios with different combination of RF input variables. We estimated the classification performance on GF-2 satellite data, compared and assessed the individual and combined contributions of GF-2 and lidar data with regard to classificatio...