A novel ensemble classifier of hyperspectral and LiDAR data using morphological features

Due to the benefits and limitation of different remote sensing sensors, fusion of the features from multiple sensors, such as hyperspectral and light detection and ranging (LiDAR) is an effective method for land cover mapping. In this paper, we propose a novel ensemble classifier to fuse hyperspectral and LiDAR datasets for classification. First, morphological features are used to model spatial and elevation information from the first few principal components (PCs) of the original hyperspetcral (HS) image and LiDAR data. Second, we split different kinds of features (i.e., spectral bands, morphological features of hyperspectral and LiDAR), into several disjoint subsets and apply the data transformation method to each subset. In particular, three data transformation methods, including principal component analysis (PCA), linearity preserving projection (LPP) and unsupervised graph fusion (UGF) are considered. Third, the features extracted in each subset are concatenated to classify by a random forest (RF) classifier. Experimental results on a co-registered HS and LiDAR data provide the effectiveness and potentialities of the proposed ensemble classifier.