Fusion of hyperspectral and LiDAR data using morphological component analysis
This paper presents a new classification framework for the fusion of hyperspectral and LiDAR data. The proposed approach aims at exploiting the complementarity of the features, i.e., textural features in the hyperspectral data and the height features in the LiDAR data, respectively. In this work, we use a morphological component analysis (MCA) method for textural feature extraction. The classification is then executed by a multinomial logistic regression classifier (MLR). Our obtained experimental results reveal that the proposed feature fusion method can lead to very good classification results.