Classification of Hyperspectral and Lidar with Deep Rotation Forest

In this work, a novel deep rotation forest is proposed to fuse hyperspectral (HS) and LiDAR. First, we extract the spatial and elevation information of two datasets by using morphological filters. Then, each feature source is applied to superpixel segmentation and then are treated as the input of deep rotation forest. In the deep rotation forest, the spatial relationships are fully considered, and the output probability of each layer is used as the input of the next layer. Experimental results demonstrate that the excellent performance of the proposed method.