Fusion of Hyperspectral and LiDAR Data With a Novel Ensemble Classifier

Abstract

Due to the development of sensors and data acquisition technology, the fusion of features from multiple sensors is a very hot topic. In this letter, the use of morphological features to fuse a hyperspectral (HS) image and a light detection and ranging (LiDAR)-derived digital surface model (DSM) is exploited via an ensemble classifier. In each iteration, we first apply morphological openings and closings with a partial reconstruction on the first few principal components (PCs) of the HS and LiDAR data sets to produce morphological features to model spatial and elevation information for HS and LiDAR data sets. Second, three groups of features (i.e., spectral and morphological features of HS and LiDAR data) are split into several disjoint subsets. Third, data transformation is applied to each subset and the features extracted in each subset are stacked as the input of a random forest classifier. Three data transformation methods, including PC analysis, linearity preserving projection, and unsupervised graph fusion, are introduced into the ensemble classification process. Finally, we integrate the classification results achieved at each step by a majority vote. Experimental results on coregistered HS and LiDAR-derived DSM demonstrate the effectiveness and potentialities of the proposed ensemble classifier.