As the advantages of both hyperspectral and LiDAR imaging, this paper explored the two source remote sensing data for classification of a urban area by deploying a 3D-deep capsule network(3D-DCN). 3D-DCN used a two-layer 3D-CNN structure at the beginning to realize non-linear feature mapping for the fused data. And it followed a capsule network to gene-rate vectors to represent features and realize encapsulation, convolution and classification. In addition, this paper proposed a nonlinear activation function named e-squash function to be used in the capsule convolution layer. The classification experiments on the urban datasets show that the LiDAR data can improve greatly the hyperspectral image classification result and the 3D-DCN with proposed activation function is of more potential than the state-in-art methods in the classification of the urban data.

[2021-08-05] Printed Article /doc/2020.09.0398/v2