Multiple Sources Data Fusion Via Deep Forest

Junshi Xia, Zuheng Ming, Akira Iwasaki

Abstract: In this paper, we propose to fuse multiple sources remotely sensed datasets, such as hyperspectral (HS) and Light Detection and Ranging (LiDAR)-derived digital surface model (DSM) using a novel deep learning method. Morphological openings and closings with partial reconstruction are taken into account to model spatial and elevation information for both sources. Then, the stacked features directly input to a deep learning classifier, namely Deep Forest (DF). In particular, Deep Forest can be viewed as the cascade or the ensembles of Rotation Forests (RoF) and Random Forests (RF). We applied the proposed method to the datasets obtained from Tama forest, Japan. Experimental results demonstrate that Deep Forest can achieve better classification results than other approaches. Compared to deep neural networks, deep forest pays little effort in parameter tuning and has a significant reduction in computational complexity.